Web services platform with cloud-based feedback control

ABSTRACT

A web services platform includes a data collector and a timeseries service. The data collector is configured to collect feedback samples provided by one or more sensors of a building management system and generate one or more feedback timeseries including a plurality of the feedback samples. The timeseries service is configured to identify a feedback control workflow that uses the feedback timeseries as an input and defines one or more processing operations to be applied to the feedback samples of the feedback timeseries, perform the one or more processing operations defined by the feedback control workflow to generate a control signal timeseries including a set of control signal samples, and provide a control signal including at least one of the control signal samples or the control signal timeseries as an output to controllable building equipment of the building management system that operate using the control signal as an input.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/142,578 filed Sep. 26, 2018 which claims the benefit of and priorityto U.S. Provisional Patent Application No. 62/564,247 filed Sep. 27,2017, and U.S. Provisional Patent Application No. 62/612,167 filed Dec.29, 2017. U.S. patent application Ser. No. 16/142,578 filed Sep. 26,2018 is a continuation-in-part of U.S. patent application Ser. No.15/644,519 filed Jul. 7, 2017, which claims the benefit of and priorityto U.S. Provisional Patent Application No. 62/457,654 filed Feb. 10,2017. U.S. patent application Ser. No. 16/142,578 filed Sep. 26, 2018 isalso a continuation-in-part of U.S. patent application Ser. No.15/644,581 filed Jul. 7, 2017, which claims the benefit of and priorityto U.S. Provisional Patent Application No. 62/457,654 filed Feb. 10,2017. U.S. patent application Ser. No. 16/142,578 filed Sep. 26, 2018 isalso a continuation-in-part of U.S. patent application Ser. No.15/644,560 filed Jul. 7, 2017, which claims the benefit of and priorityto U.S. Provisional Patent Application No. 62/457,654 filed Feb. 10,2017. The entire disclosure of each of these patent applications isincorporated by reference herein.

BACKGROUND

The present disclosure relates generally to a web services platform andmore particularly to a web services platform configured to ingest,process, and store timeseries data.

A web services platform can collect data from sensors and other types ofnetworked devices (e.g., IoT devices). Data can be collected over timeand combined into streams of timeseries data. Each sample of thetimeseries data can include a timestamp and a data value. Some webservices platforms store raw timeseries data in a relational databasewithout significant organization or processing at the time of datacollection. Applications that consume the timeseries data are typicallyresponsible for retrieving the raw timeseries data from the database andgenerating views of the timeseries data that can be presented via achart, graph, or other user interface. These processing steps aretypically performed in response to a request for the timeseries data,which can significantly delay data presentation at query time.

SUMMARY

One implementation of the present disclosure is a web services platformmonitoring and controlling equipment of a building management system.The web services platform includes a data collector and a timeseriesservice. The data collector is configured to collect feedback samplesprovided by one or more sensors of a building management system andgenerate one or more feedback timeseries including a plurality of thefeedback samples. The timeseries service is configured to identify afeedback control workflow that uses the feedback timeseries as an inputand defines one or more processing operations to be applied to thefeedback samples of the feedback timeseries, perform the one or moreprocessing operations defined by the feedback control workflow togenerate a control signal timeseries including a set of control signalsamples, and provide a control signal including at least one of thecontrol signal samples or the control signal timeseries as an output tocontrollable building equipment of the building management system thatoperate using the control signal as an input.

In some embodiments, the sensors include at least one of a temperaturesensor, a humidity sensor, a lighting sensor, an air quality sensor, oran occupancy sensor configured to sense an environmental conditionwithin a building space. In some embodiments, the sensors include atleast one of a temperature sensor, a flow rate sensor, an enthalpysensor, or a voltage sensor configured to sense an operating state orcondition of central plant equipment within a central plant. In someembodiments, the data sources include internet of things (IoT) devices.In some embodiments, the controllable building equipment include atleast one of HVAC equipment, security equipment, lighting equipment, oraccess control equipment installed within a building.

In some embodiments, generating the control signal timeseries includestransforming one or more samples of the feedback timeseries into one ormore samples of the control signal samples by applying the one or moresamples of the feedback timeseries as an input to the feedback controlworkflow and assembling the control samples to form the control signaltimeseries.

In some embodiments, the timeseries service is configured to identifyone or more other timeseries required as inputs to the feedback controlworkflow and generate an enriched feedback control workflow includingthe feedback control workflow, the feedback timeseries, and the one ormore other timeseries. In some embodiments, the one or more othertimeseries include a setpoint timeseries including a plurality ofsetpoint samples, each of the setpoint samples defining a setpointcorresponding to one of the feedback samples.

In some embodiments, the web services platform includes a timeseriesdatabase that stores a plurality of timeseries. The timeseries servicemay include a timeseries identifier configured to identify the feedbacktimeseries from the plurality of timeseries stored in the timeseriesdatabase.

In some embodiments, the web services platform includes a directedacyclic graph (DAG) database storing a plurality of feedback DAGs. Eachof the DAGs may define a feedback control workflow. In some embodiments,the timeseries service includes a DAG identifier configured to determinewhether any of the feedback control DAGs stored in the DAG database usethe feedback timeseries as an input.

In some embodiments, the timeseries service is distributed acrossmultiple systems or devices.

In some embodiments, the feedback control workflow includes at least oneof a state-based control workflow, an extremum seeking control (ESC)workflow, a proportional-integral (PI) control workflow, aproportional-integral-derivative (PID) control workflow, or a modelpredictive control (MPC) workflow that causes the timeseries service totransform the feedback timeseries into the control signal timeseriesusing a feedback control technique.

In some embodiments, the feedback control workflow comprises aproportional-integral-derivative (PID) control workflow that causes thetimeseries service to generate an error timeseries that includes aplurality of error samples. Each of the error samples may indicate adifference between one or the feedback samples and a correspondingsetpoint. The PID control workflow may cause the timeseries service togenerate the control signal timeseries by applying a set of PID controloperations to the error timeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes generating an integrated error timeseriesbased on a plurality of the error samples and generating a derivativeerror timeseries based on a change in value between consecutive samplesof the error timeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes calculating a proportional gain component bymultiplying the error timeseries by a proportional gain parameter,calculating an integral gain component by multiplying the integratederror timeseries by an integral gain parameter, calculating a derivativegain component by multiplying the derivative error timeseries by aderivative gain parameter, and combining the proportional gaincomponent, the integral gain component, and the derivative gaincomponent to generate the control signal timeseries.

Another implementation of the present disclosure is web servicesplatform for monitoring and controlling equipment of a buildingmanagement system. The web services platform includes one or morecomputer-readable storage media having instructions stored thereon that,when executed by one or more processors, cause the one or moreprocessors to collect feedback samples provided by one or more sensorsof the building management system and generate one or more feedbacktimeseries including a plurality of the feedback samples, identify afeedback control workflow that uses the feedback timeseries as an inputand defines one or more processing operations to be applied to thefeedback samples of the feedback timeseries, perform the one or moreprocessing operations defined by the feedback control workflow togenerate a control signal timeseries including a set of control signalsamples, and provide a control signal including at least one of thecontrol signal samples or the control signal timeseries as an output tocontrollable building equipment of the building management system thatoperate using the control signal as an input.

In some embodiments, the sensors include at least one of a temperaturesensor, a humidity sensor, a lighting sensor, an air quality sensor, oran occupancy sensor configured to sense an environmental conditionwithin a building space. In some embodiments, the sensors include atleast one of a temperature sensor, a flow rate sensor, an enthalpysensor, or a voltage sensor configured to sense an operating state orcondition of central plant equipment within a central plant. In someembodiments, the data sources include internet of things (IoT) devices.In some embodiments, the controllable building equipment include atleast one of HVAC equipment, security equipment, lighting equipment, oraccess control equipment installed within a building.

In some embodiments, generating the control signal timeseries includestransforming one or more samples of the feedback timeseries into one ormore samples of the control signal samples by applying the one or moresamples of the feedback timeseries as an input to the feedback controlworkflow and assembling the control samples to form the control signaltimeseries.

In some embodiments, the instructions cause the one or more processorsto identify one or more other timeseries required as inputs to thefeedback control workflow and generate an enriched feedback controlworkflow including the feedback control workflow, the feedbacktimeseries, and the one or more other timeseries. In some embodiments,the one or more other timeseries include a setpoint timeseries includinga plurality of setpoint samples, each of the setpoint samples defining asetpoint corresponding to one of the feedback samples.

In some embodiments, the web services platform includes a timeseriesdatabase that stores a plurality of timeseries. In some embodiments, theinstructions cause the one or more processors to identify the feedbacktimeseries from the plurality of timeseries stored in the timeseriesdatabase.

In some embodiments, the web services platform includes a directedacyclic graph (DAG) database storing a plurality of feedback DAGs, eachof the DAGs defining a feedback control workflow. In some embodiments,the instructions cause the one or more processors to determine whetherany of the feedback control DAGs stored in the DAG database use thefeedback timeseries as an input.

In some embodiments, the one or more processors are distributed acrossmultiple systems or devices.

In some embodiments, the feedback control workflow includes at least oneof a state-based control workflow, an extremum seeking control (ESC)workflow, a proportional-integral (PI) control workflow, aproportional-integral-derivative (PID) control workflow, or a modelpredictive control (MPC) workflow that causes the one or more processorsto transform the feedback timeseries into the control signal timeseriesusing a feedback control technique.

In some embodiments, the feedback control workflow comprises aproportional-integral-derivative (PID) control workflow that causes theone or more processors to generate an error timeseries that includes aplurality of error samples. Each of the error samples may indicate adifference between one or the feedback samples and a correspondingsetpoint. The PID control workflow may cause the one or more processorsto generate the control signal timeseries by applying a set of PIDcontrol operations to the error timeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes generating an integrated error timeseriesbased on a plurality of the error samples and generating a derivativeerror timeseries based on a change in value between consecutive samplesof the error timeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes calculating a proportional gain component bymultiplying the error timeseries by a proportional gain parameter,calculating an integral gain component by multiplying the integratederror timeseries by an integral gain parameter, calculating a derivativegain component by multiplying the derivative error timeseries by aderivative gain parameter, and combining the proportional gaincomponent, the integral gain component, and the derivative gaincomponent to generate the control signal timeseries.

Another implementation of the present disclosure is a method formonitoring and controlling equipment of a building management system.The method includes collecting feedback samples provided by one or moresensors of the building management system and generating one or morefeedback timeseries including a plurality of the feedback samples,identifying a feedback control workflow that uses the feedbacktimeseries as an input and defines one or more processing operations tobe applied to the feedback samples of the feedback timeseries,performing the one or more processing operations defined by the feedbackcontrol workflow to generate a control signal timeseries including a setof control signal samples, and providing a control signal including atleast one of the control signal samples or the control signal timeseriesas an output to controllable building equipment of the buildingmanagement system that operate using the control signal as an input.

In some embodiments, the sensors include at least one of a temperaturesensor, a humidity sensor, a lighting sensor, an air quality sensor, oran occupancy sensor configured to sense an environmental conditionwithin a building space. In some embodiments, the sensors include atleast one of a temperature sensor, a flow rate sensor, an enthalpysensor, or a voltage sensor configured to sense an operating state orcondition of central plant equipment within a central plant. In someembodiments, the data sources include internet of things (IoT) devices.In some embodiments, the controllable building equipment include atleast one of HVAC equipment, security equipment, lighting equipment, oraccess control equipment installed within a building.

In some embodiments, generating the control signal timeseries includestransforming one or more samples of the feedback timeseries into one ormore samples of the control signal samples by applying the one or moresamples of the feedback timeseries as an input to the feedback controlworkflow and assembling the control samples to form the control signaltimeseries and assembling the control samples to form the control signaltimeseries.

In some embodiments, the method includes identifying one or more othertimeseries required as inputs to the feedback control workflow andgenerating an enriched feedback control workflow including the feedbackcontrol workflow, the feedback timeseries, and the one or more othertimeseries. In some embodiments, the one or more other timeseriesinclude a setpoint timeseries including a plurality of setpoint samples,each of the setpoint samples defining a setpoint corresponding to one ofthe feedback samples.

In some embodiments, the method includes accessing a timeseries databasethat stores a plurality of timeseries and identifying the feedbacktimeseries from the plurality of timeseries stored in the timeseriesdatabase.

In some embodiments, the method includes accessing a directed acyclicgraph (DAG) database that stores a plurality of feedback DAGs, each ofthe DAGs defining a feedback control workflow, and determining whetherany of the feedback control DAGs stored in the DAG database use thefeedback timeseries as an input.

In some embodiments, the one or more processing operations defined bythe feedback control workflow are distributed across multiple systems ordevices.

In some embodiments, the feedback control workflow includes at least oneof a state-based control workflow, an extremum seeking control (ESC)workflow, a proportional-integral (PI) control workflow, aproportional-integral-derivative (PID) control workflow, or a modelpredictive control (MPC) workflow that causes the feedback timeseries tobe transformed into the control signal timeseries using a feedbackcontrol technique.

In some embodiments, the feedback control workflow comprises aproportional-integral-derivative (PID) control workflow. Performing theone or more processing operations defined by the feedback controlworkflow may include generating an error timeseries that includes aplurality of error samples. Each of the error samples may indicate adifference between one or the feedback samples and a correspondingsetpoint. Performing the one or more processing operations defined bythe feedback control workflow may include generating the control signaltimeseries by applying a set of PID control operations to the errortimeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes generating an integrated error timeseriesbased on a plurality of the error samples and generating a derivativeerror timeseries based on a change in value between consecutive samplesof the error timeseries.

In some embodiments, applying the set of PID control operations to theerror timeseries includes calculating a proportional gain component bymultiplying the error timeseries by a proportional gain parameter,calculating an integral gain component by multiplying the integratederror timeseries by an integral gain parameter, calculating a derivativegain component by multiplying the derivative error timeseries by aderivative gain parameter, and combining the proportional gaincomponent, the integral gain component, and the derivative gaincomponent to generate the control signal timeseries.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a web services system including a webservices platform, according to some embodiments.

FIG. 2 is a block diagram illustrating the web services platform of FIG.1 in greater detail including a data collector, platform services, andapplications, according to some embodiments.

FIG. 3 is a block diagram of a timeseries service which can beimplemented as some of the platform services shown in FIG. 2, accordingto some embodiments.

FIG. 4 is a block diagram illustrating an aggregation technique whichcan be used by the sample aggregator shown in FIG. 3 to aggregate rawdata samples, according to some embodiments.

FIG. 5 is a data table which can be used to store raw data timeseriesand a variety of derived data timeseries which can be generated by thetimeseries service of FIG. 3, according to some embodiments.

FIG. 6 is a drawing of several timeseries illustrating thesynchronization of data samples which can be performed by the dataaggregator shown in FIG. 3, according to some embodiments.

FIG. 7 is a flow diagram illustrating the creation and storage of afault detection timeseries which can be performed by the fault detectorshown in FIG. 3, according to some embodiments.

FIG. 8 is a data table which can be used to store the raw datatimeseries and the fault detection timeseries, according to someembodiments.

FIG. 9A is a data table which can be used to store states assigned tosamples of a data timeseries, according to some embodiments.

FIG. 9B is a data table including various events generated based on theassigned states shown in the table of FIG. 9A, according to someembodiments.

FIG. 9C is a flowchart of a process for generating and updating eventsand eventseries, according to some embodiments.

FIG. 10A is a directed acyclic graph (DAG) which can be generated by theDAG generator of FIG. 3, according to some embodiments.

FIG. 10B is a code snippet which can be automatically generated by theDAG generator of FIG. 3 based on the DAG, according to some embodiments.

FIG. 11A is an entity graph illustrating relationships between anorganization, a space, a system, a point, and a timeseries, which can beused by the data collector of FIG. 2, according to some embodiments.

FIG. 11B is an example of an entity graph for a particular system ofdevices, according to some embodiments.

FIG. 12 is an object relationship diagram illustrating relationshipsbetween an entity template, a point, a timeseries, and a data sample,which can be used by the data collector of FIG. 2 and the timeseriesservice of FIG. 3, according to some embodiments.

FIG. 13A is a block diagram illustrating a timeseries processingworkflow which can be performed by the timeseries service of FIGS. 2-3,according to some embodiments.

FIG. 13B is a flowchart of a process which can be performed by theworkflow manager of FIG. 13A, according to some embodiments.

FIG. 14 is a block diagram of a system for processing streaming data,which may be implemented as part of the web services platform of FIG. 2,according to some embodiments.

FIG. 15A is a block diagram illustrating an iterative timeseriesprocessing technique used by the system of FIG. 14, according to someembodiments.

FIG. 15B is a flowchart of an iterative timeseries processing processwhich can be performed by the system of FIG. 14, according to someembodiments.

FIG. 16 is a block diagram of a cloud-based feedback control systemincluding the web services platform of FIG. 2, according to someembodiments.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, a web services platform with nestedstream generation is shown, according to various embodiments. The webservices platform is configured to collect data samples from a varietyof data sources (e.g., sensors, controllable devices, IoT devices, etc.)and generate raw timeseries data from the data samples. The web servicesplatform can process the raw timeseries data using a variety of platformservices to generate derived timeseries data (e.g., data rolluptimeseries, virtual point timeseries, fault detection timeseries, etc.).The derived timeseries data can be provided to various applicationsand/or stored in local or hosted storage. In some embodiments, the webservices platform includes three different layers that separate (1) datacollection, (2) data storage, retrieval, and analysis, and (3) datavisualization. This allows the web services platform to support avariety of applications that use the derived timeseries data and allowsnew applications to reuse the infrastructure provided by the platformservices.

In some embodiments, the web services platform includes a data collectorconfigured to collect raw data samples from the data sources. The datacollector can generate a raw data timeseries including a plurality ofthe raw data samples and store the raw data timeseries in the timeseriesdatabase. In some embodiments, the data collector stores each of the rawdata samples with a timestamp. The timestamp can include a local timeindicating the time at which the raw data sample was collected inwhichever time zone the raw data sample was collected. The timestamp canalso include a time offset indicating a difference between the localtime and universal time. The combination of the local timestamp and theoffset provides a unique timestamp across daylight saving timeboundaries. This allows an application using the timeseries data todisplay the timeseries data in local time without first converting fromuniversal time. The combination of the local timestamp and the offsetalso provides enough information to convert the local timestamp touniversal time without needing to look up a schedule of when daylightsavings time occurs.

In some embodiments, the platform services include a sample aggregator.The sample aggregator can aggregate predefined intervals of the rawtimeseries data (e.g., quarter-hourly intervals, hourly intervals, dailyintervals, monthly intervals, etc.) to generate new derived timeseriesof the aggregated values. These derived timeseries can be referred to as“data rollups” since they are condensed versions of the raw timeseriesdata. The data rollups generated by the data aggregator provide anefficient mechanism for various applications to query the timeseriesdata. For example, the applications can construct visualizations of thetimeseries data (e.g., charts, graphs, etc.) using the pre-aggregateddata rollups instead of the raw timeseries data. This allows theapplications to simply retrieve and present the pre-aggregated datarollups without requiring applications to perform an aggregation inresponse to the query. Since the data rollups are pre-aggregated, theapplications can present the data rollups quickly and efficientlywithout requiring additional processing at query time to generateaggregated timeseries values.

In some embodiments, the platform services include a virtual pointcalculator. The virtual point calculator can calculate virtual pointsbased on the raw timeseries data and/or the derived timeseries data.Virtual points can be calculated by applying any of a variety ofmathematical operations (e.g., addition, subtraction, multiplication,division, etc.) or functions (e.g., average value, maximum value,minimum value, thermodynamic functions, linear functions, nonlinearfunctions, etc.) to the actual data points represented by the timeseriesdata. For example, the virtual point calculator can calculate a virtualdata point (pointID₃) by adding two or more actual data points (pointID₁and pointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,the virtual point calculator can calculate an enthalpy data point(pointID₄) based on a measured temperature data point (pointID₅) and ameasured pressure data point (pointID₆) (e.g.,pointID₄=enthalpy(pointID₅, pointID₆)). The virtual data points can bestored as derived timeseries data.

Applications can access and use the virtual data points in the samemanner as the actual data points. The applications do not need to knowwhether a data point is an actual data point or a virtual data pointsince both types of data points can be stored as derived timeseries dataand can be handled in the same manner by the applications. In someembodiments, the derived timeseries data are stored with attributesdesignating each data point as either a virtual data point or an actualdata point. Such attributes allow the applications to identify whether agiven timeseries represents a virtual data point or an actual datapoint, even though both types of data points can be handled in the samemanner by the applications.

In some embodiments, the platform services include a fault detectorconfigured to analyze the timeseries data to detect faults. Faultdetection can be performed by applying a set of fault detection rules tothe timeseries data to determine whether a fault is detected at eachinterval of the timeseries. Fault detections can be stored as derivedtimeseries data. For example, new timeseries can be generated with datavalues that indicate whether a fault was detected at each interval ofthe timeseries. The time series of fault detections can be stored alongwith the raw timeseries data and/or derived timeseries data in local orhosted data storage.

In some embodiments, the web services platform collects feedback samples(e.g., measurements, samples of monitored variables, system states,values of points, etc.) from sensors of a building management system.The web services platform uses the feedback samples as an input to acloud-based feedback control algorithm (e.g., PID, MPC, etc.) that usesthe feedback samples to generate control signal samples. In someembodiments, the web services platform treats the feedback samples assamples of an input timeseries and processes the input timeseries usinga feedback control workflow. The feedback control workflow converts thefeedback samples into control signal samples, which are a type ofderived timeseries samples. The control signal samples are then providedas a control signal to controllable equipment of the building managementsystem. These and other features of the web services platform aredescribed in greater detail below.

Web Services System

Referring now to FIG. 1, a block diagram of a web services system 100 isshown, according to an exemplary embodiment. Web services system 100 isshown to include a web services platform 102. Web services platform 102can be configured to collect data from a variety of different datasources. For example, web services platform 102 is shown collecting datafrom a variety of devices 112-116, 122-126, 132-136, and 142-146. Insome embodiments, devices 112-116, 122-126, 132-136, and 142-146 areinternet of things (IoT) devices. Several examples of IoT devices whichcan provide data to web services platform 102 are described in detailwith reference to FIG. 2. While the devices described herein aregenerally referred to as IoT devices, it should be understood that, invarious embodiments, the devices references in the present disclosurecould be any type of devices capable to communication of data over anelectronic network.

Web services platform 102 can collect data from a variety of externalsystems or services. For example, web services platform 102 is shownreceiving weather data from a weather service 152, news data from a newsservice 154, documents and other document-related data from a documentservice 156, and media (e.g., video, images, audio, social media, etc.)from a media service 158. In some embodiments, web services platform 102generates data internally. For example, web services platform 102 mayinclude a web advertising system, a website traffic monitoring system, aweb sales system, or other types of platform services that generatedata. The data generated by web services platform 102 can be collected,stored, and processed along with the data received from other datasources. Web services platform 102 can collect data directly fromexternal systems or devices or via a network 104 (e.g., a WAN, theInternet, a cellular network, etc.). Web services platform 102 canprocess and transform collected data to generate timeseries data andentity data. Several features of web services platform 102 are describedin detail below.

Web Services Platform

Referring now to FIG. 2, a block diagram illustrating web servicesplatform 102 in greater detail is shown, according to some embodiments.Web services platform 102 can be configured to collect data from avariety of different data sources. For example, web services platform102 is shown collecting data from information systems 202, internet ofthings (IoT) devices 203, weather service 152, news service 154,document service 156, and media service 158. In some embodiments, webservices platform 102 separates data collection/ingestion; data storage,retrieval, and analysis; and data visualization into three differentlayers. This allows web services platform 102 to support a variety ofapplications 230 that use the data and allows new applications 230 toreuse the existing infrastructure provided by platform services 220.

Information systems 202 can also include any type of system configuredto manage information associated with any of a variety of devices,systems, people and/or the activities thereof. For example, informationsystems 202 can include a human resources (HR) system, an accountingsystem, a payroll system, a customer relationship management (CRM)system, a marketing system, an enterprise resource planning system, orany other type of system that can be used to manage devices, systems,people, and/or the information associated therewith.

IoT devices 203 may include any of a variety of physical devices,sensors, actuators, electronics, vehicles, home appliances, and/or otheritems having network connectivity which enable IoT devices 203 tocommunicate with web services platform 102. For example, IoT devices 203can include smart home hub devices, smart house devices, doorbellcameras, air quality sensors, smart switches, smart lights, smartappliances, garage door openers, smoke detectors, heart monitoringimplants, biochip transponders, cameras streaming live feeds,automobiles with built-in sensors, DNA analysis devices, field operationdevices, tracking devices for people/vehicles/equipment, networkedsensors, wireless sensors, wearable sensors, environmental sensors, RFIDgateways and readers, IoT gateway devices, robots and other roboticdevices, GPS devices, smart watches, virtual/augmented reality devices,and/or other networked or networkable devices. In some embodiments, IoTdevices 203 include some or all of devices 112-116, 122-126, 132-136,and 142-146, as described with reference to FIG. 1.

Weather service 152, news service 154, document service 156, and mediaservice 158 may be the same as previously described. For example,weather service 152 can be configured to provide weather data to webservices platform 102. News service 154 can be configured to providenews data to web services platform 102. Document service 156 can beconfigured to provide documents and other document-related data to webservices platform 102. Media service 158 can be configured to providemedia (e.g., video, images, audio, social media, etc.) to web servicesplatform 102. In some embodiments, media service 158 includes aninternet-based advertising system or click tracking system. For example,media service 158 can provide event data to web services platform 102 inresponse to a web server delivering a webpage, advertisement, orreceiving a click from a user. Web services platform 102 can beconfigured to ingest, process, store, and/or publish data from these andany of a variety of other data sources.

Web services platform 102 is shown receiving two main types of data:information technology (IT) data and operational technology (OT) data.IT data may include data that describes various entities (e.g., people,spaces, devices, etc.) and the relationships therebetween. For example,IT data may include an entity graph that describes the relationshipsbetween spaces, equipment, and other entities (e.g., person A ownsdevice B, device B controls device C, sensor D provides input to deviceC, person E is part of employee team F, floor G contains room C, etc.).IT data may include human resources data that describes a set ofemployees and includes details about the employees (e.g., name, employeeID, job title/role, responsibilities, payroll information, address,etc.). IT data may include IoT device information (e.g., devicelocations, descriptions, device relationships, etc.), and/or otherinformation that provides context for the data received by web servicesplatform 102 or describes the entities managed by web services platform102. In some embodiments, IT data is preexisting/static and can beprovided to web services platform 102 as a batch. However, it iscontemplated that IT data can be updated after it has been created ifchanges occur to the entities or relationships described by the IT data.

As used herein, the term “static” refers to data, characteristics,attributes, or other information that does not change over time orchange infrequently. For example, a device name or address may bereferred to as a static characteristic of the device because it does notchange frequently. However, should be understood that “static” items arenot limited to permanently fixed information. Some types of static itemsmay change occasionally or infrequently. For example, a device addressmay be a type of static attribute that can be changed if desired but isnot expected to change frequently. Static data is contrasted withdynamic data that is expected to change relatively frequently.

OT data may include data that is generated and/or updated in real-timeas a result of operating the systems and devices that provide data toweb services platform 102. For example, OT data may include timeseriesdata received from IoT devices 203 (e.g., sensor measurements, statusindications, alerts, notifications, etc.), weather information receivedfrom weather service 152, a news feed received from news service 154,document updates received from document service 156, media updatesreceived from media service 158, and/or other types of telemetry data.In general, OT data can be described as real-time operational data,dynamic data, or streaming data, whereas IT data can be described asinstitutional or contextual data that is not continuously updated. Forexample, the OT data associated with a particular sensor may includemeasurements from the sensor, whereas the IT data associated with thesensor may include the sensor name, sensor type, and sensor location.

Web services platform 102 can process and transform/translate the OTdata and IT data using platform services 220 to generate timeseries dataand entity data. Throughout this disclosure, the term “raw timeseriesdata” is used to describe the raw data samples of OT data received byweb services platform 102. The term “derived timeseries data” is used todescribe the result or output of a transformation or other timeseriesprocessing operation performed by platform services 220 (e.g., dataaggregation, data cleansing, virtual point calculation, etc.). The rawtimeseries data and derived timeseries data can be provided to variousapplications 230 and/or stored in timeseries storage 214 (e.g., asmaterialized views of the raw timeseries data). The term “entity data”is used to describe the attributes of various entities (e.g., people,spaces, things, etc.) and relationships between entities. The entitydata can be created by platform services 220 as a result of processingthe IT data and/or OT data received by web services platform 102 and canbe stored in entity storage 216.

Before discussing web services platform 102 in greater detail, it shouldbe noted that the components of web services platform 102 can beintegrated within a single device (e.g., a web server, a supervisorycontroller, a computing system, etc.) or distributed across multipleseparate systems or devices. For example, the components of web servicesplatform 102 can be implemented as part of a cloud computing platformconfigured to receive and process data from multiple IoT devices andother data sources. In other embodiments, the components of web servicesplatform 102 can be implemented as part of a suite of cloud-hostedservices. In other embodiments, some or all of the components of webservices platform 102 can be components of a subsystem level controller,a plant controller, a device controller, a field controller, a computerworkstation, a client device, or any other system or device thatreceives and processes data from IoT devices or other data sources.

Still referring to FIG. 2, web services platform 102 is shown to includea communications interface 204. Communications interface 204 can includewired or wireless communications interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with information systems 202, IoT devices203, weather service 152, news service 154, document service 156, mediaservice 158, or other external systems or devices. Communicationsconducted via communications interface 204 can be direct (e.g., localwired or wireless communications) or via a communications network 104(e.g., a WAN, the Internet, a cellular network, etc.).

Communications interface 204 can facilitate communications between webservices platform 102 and external applications (e.g., remote systemsand applications) for allowing user control, monitoring, and adjustmentto web services platform 102 and/or the devices that communicate withweb services platform 102. Communications interface 204 can alsofacilitate communications between web services platform 102 and clientdevices (e.g., computer workstations, laptop computers, tablets, mobiledevices, etc.). Web services platform 102 can be configured tocommunicate with external systems and devices using any of a variety ofcommunications protocols (e.g., HTTP(S), WebSocket, CoAP, MQTT, etc.),industrial control protocols (e.g., MTConnect, OPC, OPC-UA, etc.),process automation protocols (e.g., HART, Profibus, etc.), homeautomation protocols, or any of a variety of other protocols.Advantageously, web services platform 102 can receive, ingest, andprocess data from any type of system or device regardless of thecommunications protocol used by the system or device.

Web services platform 102 is shown to include a processing circuit 206including a processor 208 and memory 210. Processor 208 can be a generalpurpose or specific purpose processor, an application specificintegrated circuit (ASIC), one or more field programmable gate arrays(FPGAs), a group of processing components, or other suitable processingcomponents. Processor 208 is configured to execute computer code orinstructions stored in memory 210 or received from other computerreadable media (e.g., CDROM, network storage, a remote server, etc.).

Memory 210 can include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 210 can include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory210 can include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 210 can be communicably connected toprocessor 208 via processing circuit 206 and can include computer codefor executing (e.g., by processor 208) one or more processes describedherein. When processor 208 executes instructions stored in memory 210,processor 208 generally configures processing circuit 206 to completesuch activities.

In some embodiments, web services platform 102 includes a plurality ofprocessors, memories, interfaces, and other components distributedacross multiple devices or systems. For example, in a cloud-based ordistributed implementation, web services platform 102 may includemultiple discrete computing devices, each of which includes a processor208, memory 210, communications interface 204, data collector 212,and/or other components of web services platform 102. Tasks performed byweb services platform 102 can be distributed across multiple systems ordevices, which may be located within a single building or facility ordistributed across multiple buildings or facilities. In someembodiments, multiple data collectors 212 are implemented usingdifferent processors, computing devices, servers, and/or othercomponents and carry out portions of the features described herein.

Still referring to FIG. 2, web services platform 102 is shown to includea data collector 212. Data collector 212 can receive the IT data and OTdata via communications interface 204 and can provide translated IT dataand OT data to platform services 220, timeseries storage 214, and/orentity storage 216. For example, data collector 212 can be configured totranslate the incoming IT data and OT data from a protocol or formatused by the data sources into a protocol or format used by platformservices 220. In some embodiments, the OT data include timestamps anddata values for various data points. The data values can be measured orcalculated values, depending on the type of data point. For example, adata point received from a temperature sensor can include a measureddata value indicating a temperature measured by the temperature sensor.A data point received from a device controller can include a calculateddata value indicating a calculated efficiency of the device. Datacollector 212 can receive data samples from multiple different devices.

The data samples can include one or more attributes that describe orcharacterize the corresponding data points. For example, the datasamples can include a name attribute defining a point name or ID (e.g.,“B1F4R2.T-Z”), a device attribute indicating a type of device from whichthe data samples is received (e.g., temperature sensor, humidity sensor,pressure sensor, etc.), a unit attribute defining a unit of measureassociated with the data value (e.g., ° F., ° C., kPA, etc.), and/or anyother attribute that describes the corresponding data point or providescontextual information regarding the data point. The types of attributesincluded in each data point can depend on the communications protocolused to send the data samples to web services platform 102. For example,data samples received via a first protocol can include a variety ofdescriptive attributes along with the data value, whereas data samplesreceived via the second protocol may include a lesser number ofattributes (e.g., only the data value without any correspondingattributes).

In some embodiments, each data sample is received with a timestampindicating a time at which the corresponding data value was measured orcalculated. In other embodiments, data collector 212 adds timestamps tothe data samples based on the times at which the data samples arereceived. Data collector 212 can generate raw timeseries data for eachof the data points for which data samples are received. Each timeseriescan include a series of data values for the same data point and atimestamp for each of the data values. For example, a timeseries for adata point provided by a temperature sensor can include a series oftemperature values measured by the temperature sensor and thecorresponding times at which the temperature values were measured. Anexample of a timeseries which can be generated by data collector 212 isas follows:

[<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key, timestamp₃,value₃>]where key is an identifier of the source of the raw data samples (e.g.,timeseries ID, sensor ID, etc.), timestamp_(i) identifies the time atwhich the ith sample was collected, and value_(i) indicates the value ofthe ith sample.

Data collector 212 can add timestamps to the data samples or modifyexisting timestamps such that each data sample includes a localtimestamp. Each local timestamp indicates the local time at which thecorresponding data sample was measured or collected and can include anoffset relative to universal time. The local timestamp indicates thelocal time at the location the data point was measured at the time ofmeasurement. The offset indicates the difference between the local timeand a universal time (e.g., the time at the international date line).For example, a data sample collected in a time zone that is six hoursbehind universal time can include a local timestamp (e.g.,Timestamp=2016-03-18T14:10:02) and an offset indicating that the localtimestamp is six hours behind universal time (e.g., Offset=−6:00). Theoffset can be adjusted (e.g., +1:00 or −1:00) depending on whether thetime zone is in daylight savings time when the data sample is measuredor collected.

The combination of the local timestamp and the offset provides a uniquetimestamp across daylight saving time boundaries. This allows anapplication using the timeseries data to display the timeseries data inlocal time without first converting from universal time. The combinationof the local timestamp and the offset also provides enough informationto convert the local timestamp to universal time without needing to lookup a schedule of when daylight savings time occurs. For example, theoffset can be subtracted from the local timestamp to generate auniversal time value that corresponds to the local timestamp withoutreferencing an external database and without requiring any otherinformation.

In some embodiments, data collector 212 organizes the raw timeseriesdata. Data collector 212 can identify a system or device associated witheach of the data points. For example, data collector 212 can associate adata point with an IoT device, a sensor, a networking device, or anyother type of system or device. In various embodiments, data collector212 uses the name of the data point, a range of values of the datapoint, statistical characteristics of the data point, or otherattributes of the data point to identify a particular system or deviceassociated with the data point. Data collector 212 can then determinehow that system or device relates to the other systems or devices. Forexample, data collector 212 can determine that the identified system ordevice is part of a larger system (e.g., a vehicle control system) or isassociated with a particular space (e.g., a particular factory, a roomor zone of the factory, etc.). In some embodiments, data collector 212uses or creates an entity graph when organizing the timeseries data. Anexample of such an entity graph is described in greater detail withreference to FIG. 12.

In some embodiments, data collector 212 uses the IT data and OT data toupdate the attributes of various entities. As described above, an entityis a virtual representation (e.g., a data object) of a person, space,system, device, or thing that provides data to web services platform102. For example, a vehicle entity may be a virtual representation of aphysical vehicle (e.g., a car, truck, airplane, boat, etc.). The vehicleentity may include a variety of attributes that describe the vehicle.For example, the vehicle may include a “location” attribute thatdescribes where the vehicle is located, a “contains” attribute thatidentifies one or more systems or devices of equipment contained withinthe vehicle, a “temperature” attribute that indicates the current airtemperature within the vehicle, an “occupancy” attribute that indicateswhether the vehicle is occupied or unoccupied, or any of a variety ofother attributes. Data collector 212 can use the OT data to update thevalues of the attributes of various entities each time a new data sampleor event is received. Similarly, data collector 212 can use the IT datato update the values of the attributes of various entities when therelationships between entities or other attributes indicated by the ITdata changes. In other embodiments, entity attributes are updated byentity service 226 of platform services 220.

Data collector 212 can provide the timeseries data and entity data toplatform services 220 and/or store the timeseries data and entity datain timeseries storage 214 and entity storage 216, respectively. In someembodiments, timeseries storage 214 and entity storage 216 can be datastorage internal to web services platform 102 (e.g., within memory 210)or other on-site data storage local to the location at which the IT dataand OT data are collected. In other embodiments, timeseries storage 214and entity storage 216 can include a remote database, cloud-based datahosting, or other remote data storage. For example, timeseries storage214 and entity storage 216 can include remote data storage locatedoff-site relative to the location at which the IT data and OT data arecollected. Timeseries storage 214 can be configured to store the rawtimeseries data obtained by data collector 212, and/or the derivedtimeseries data generated by platform services 220. Similarly, entitystorage 216 can be configured to store the IT data and OT data collectedby data collector 212 and/or the entity data generated by platformservices 220. Directed acyclic graphs (DAGs) used by platform services220 to process the timeseries data can be stored in DAG storage 330(shown in FIG. 3).

Still referring to FIG. 2, web services platform 102 is shown to includeplatform services 220. Platform services 220 can receive the translatedIT data and OT data from data collector 212 and/or retrieve thetimeseries data and entity data from timeseries storage 214 and entitystorage 216. Platform services 220 can include a variety of servicesconfigured to analyze, process, and transform the IT data and OT data tocreate timeseries data and entity data. For example, platform services220 are shown to include a security service 222, an analytics service224, an entity service 226, and a timeseries service 228. Securityservice 222 can assign security attributes to the IT data and OT data toensure that the IT data and OT data are only accessible to authorizedindividuals, systems, or applications.

Analytics service 224 can use the translated IT data and OT data asinputs to various analytics (e.g., fault detection, energy consumption,web traffic, revenue, etc.) to derive an analytic result from the ITdata and OT data. Analytics service 224 can apply a set of faultdetection rules to the IT data and OT data to determine whether a faultis detected at each interval of a timeseries. Fault detections can bestored as derived timeseries data. For example, analytics service 224can generate a new fault detection timeseries with data values thatindicate whether a fault was detected at each interval of thetimeseries. The fault detection timeseries can be stored as derivedtimeseries data along with the raw timeseries data in timeseries storage214.

Entity service 226 can use the translated IT data and OT data providedby data collector 212 to create or update the attributes of variousentities managed by web services platform 102. Some entity attributesmay be the most recent value of a data point provided to web servicesplatform 102 as OT data. For example, the “temperature” attribute of avehicle entity may be the most recent value of a temperature data pointprovided by a temperature sensor located in the vehicle. Entity service226 can use the IT data to identify the temperature sensor located inthe vehicle and can use the OT data associated with the identifiedtemperature sensor to update the “temperature” attribute each time a newsample of the temperature data point is received. As another example, a“most recent view” attribute of a webpage entity may indicate the mostrecent time at which the webpage was viewed. Entity service 226 can usethe OT data from a click tracking system or web server to determine whenthe most recent view occurred and can update the “most recent view”attribute accordingly.

Other entity attributes may be the result of an analytic,transformation, calculation, or other processing operation based on theOT data and IT data. For example, entity service 226 can use the IT datato identify an access control device (e.g., an electronic lock, akeypad, etc.) at the entrance/exit of a vehicle. Entity service 226 canuse OT data received from the identified access control device to trackthe number of occupants entering and exiting the vehicle. Entity service226 can update a “number of occupants” attribute of an entityrepresenting the vehicle each time a person enters or exits the vehiclesuch that the “number of occupants” attribute reflects the currentnumber of occupants within the vehicle. As another example, a “totalrevenue” attribute associated with a product line entity may be thesummation of all the revenue generated from sales of the correspondingproduct. Entity service 226 can use the OT data received from a salestracking system (e.g., a point of sale system, an accounting database,etc.) to determine when a sale of the product occurs and identify theamount of revenue generated by the sale. Entity service 226 can thenupdate the “total revenue” attribute by adding the most recent salesrevenue to the previous value of the attribute.

In some embodiments, entity service 226 uses IT data and/or OT data frommultiple different data sources to update the attributes of variousentities. For example, an entity representing a person may include a“risk” attribute that quantifies the person's level of risk attributableto various physical, environmental, or other conditions. Entity service226 can use OT data from a card reader or IT data from a human resourcessystem to determine the physical location of the person at any giventime. Entity service 226 can use weather data from weather service 152to determine whether any severe weather is approaching the person'slocation. Similarly, entity service 226 can use emergency data from newsservice 154 or media service 158 to determine whether the person'slocation is experiencing any emergency conditions (e.g., active shooter,police response, fire response, etc.). Entity service 226 can use datafrom information systems 202 to determine whether the person's locationis experiencing any emergency conditions (e.g., fire, building lockdown,etc.) or environmental hazards (e.g., detected air contaminants,pollutants, extreme temperatures, etc.) that could increase the person'slevel of risk. Entity service 226 can use these and other types of dataas inputs to a risk function that calculates the value of the person's“risk” attribute and can update the person entity accordingly.

Still referring to FIG. 2, timeseries service 228 can apply varioustransformations, operations, or other functions to the raw timeseriesdata provided by data collector 212 to generate derived timeseries data.In some embodiments, timeseries service 228 aggregates predefinedintervals of the raw timeseries data (e.g., quarter-hourly intervals,hourly intervals, daily intervals, monthly intervals, etc.) to generatenew derived timeseries of the aggregated values. These derivedtimeseries can be referred to as “data rollups” since they are condensedversions of the raw timeseries data. The data rollups generated bytimeseries service 228 provide an efficient mechanism for applications230 to query the timeseries data. For example, applications 230 canconstruct visualizations of the timeseries data (e.g., charts, graphs,etc.) using the pre-aggregated data rollups instead of the rawtimeseries data. This allows applications 230 to simply retrieve andpresent the pre-aggregated data rollups without requiring applications230 to perform an aggregation in response to the query. Since the datarollups are pre-aggregated, applications 230 can present the datarollups quickly and efficiently without requiring additional processingat query time to generate aggregated timeseries values.

In some embodiments, timeseries service 228 calculates virtual pointsbased on the raw timeseries data and/or the derived timeseries data.Virtual points can be calculated by applying any of a variety ofmathematical operations (e.g., addition, subtraction, multiplication,division, etc.) or functions (e.g., average value, maximum value,minimum value, thermodynamic functions, linear functions, nonlinearfunctions, etc.) to the actual data points represented by the timeseriesdata. For example, timeseries service 228 can calculate a virtual datapoint (pointID₃) by adding two or more actual data points (pointID₁ andpointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,timeseries service 228 can calculate an enthalpy data point (pointID₄)based on a measured temperature data point (pointID₂) and a measuredpressure data point (pointID₆) (e.g., pointID₄=enthalpy(pointID₂,pointID₆)). The virtual data points can be stored as derived timeseriesdata.

Applications 230 can access and use the virtual data points in the samemanner as the actual data points. Applications 230 do not need to knowwhether a data point is an actual data point or a virtual data pointsince both types of data points can be stored as derived timeseries dataand can be handled in the same manner by applications 230. In someembodiments, the derived timeseries are stored with attributesdesignating each data point as either a virtual data point or an actualdata point. Such attributes allow applications 230 to identify whether agiven timeseries represents a virtual data point or an actual datapoint, even though both types of data points can be handled in the samemanner by applications 230.

Still referring to FIG. 2, web services platform 102 is shown to includeseveral applications 230 including an energy management application 232,monitoring and reporting applications 234, and enterprise controlapplications 236. Although only a few applications 230 are shown, it iscontemplated that applications 230 can include any of a variety ofapplications configured to use the derived timeseries generated byplatform services 220. In some embodiments, applications 230 exist as aseparate layer of web services platform 102 (i.e., separate fromplatform services 220 and data collector 212). This allows applications230 to be isolated from the details of how the IT data and OT data arecollected and how the timeseries data and entity data are generated. Inother embodiments, applications 230 can exist as remote applicationsthat run on remote systems or devices (e.g., remote systems andapplications, client devices, etc.).

Applications 230 can use the derived timeseries data to perform avariety data visualization, monitoring, and/or control activities. Forexample, energy management application 232 and monitoring and reportingapplication 234 can use the derived timeseries data to generate userinterfaces (e.g., charts, graphs, etc.) that present the derivedtimeseries data to a user. In some embodiments, the user interfacespresent the raw timeseries data and the derived data rollups in a singlechart or graph. For example, a dropdown selector can be provided toallow a user to select the raw timeseries data or any of the datarollups for a given data point.

Enterprise control application 236 can use the derived timeseries datato perform various control activities. For example, enterprise controlapplication 236 can use the derived timeseries data as input to acontrol algorithm (e.g., a state-based algorithm, an extremum seekingcontrol (ESC) algorithm, a proportional-integral (PI) control algorithm,a proportional-integral-derivative (PID) control algorithm, a modelpredictive control (MPC) algorithm, a feedback control algorithm, etc.)to generate control signals for IoT devices 203.

Timeseries Platform Service

Referring now to FIG. 3, a block diagram illustrating timeseries service228 in greater detail is shown, according to some embodiments.Timeseries service 228 is shown to include a timeseries web service 302,an events service 303, a timeseries processing engine 304, and atimeseries storage interface 316. Timeseries web service 302 can beconfigured to interact with web-based applications to send and/orreceive timeseries data. In some embodiments, timeseries web service 302provides timeseries data to web-based applications. For example, if oneor more of applications 230 are web-based applications, timeseries webservice 302 can provide derived timeseries data and raw timeseries datato the web-based applications. In some embodiments, timeseries webservice 302 receives raw timeseries data from a web-based datacollector. For example, if data collector 212 is a web-basedapplication, timeseries web service 302 can receive data samples or rawtimeseries data from data collector 212.

Timeseries storage interface 316 can be configured to store and readsamples of various timeseries (e.g., raw timeseries data and derivedtimeseries data) and eventseries (described in greater detail below).Timeseries storage interface 316 can interact with timeseries storage214, eventseries storage 329, and/or DAG storage 330. In someembodiments, timeseries storage interface 316 can read samples fromtimeseries storage 214 from a specified start time or start position inthe timeseries to a specified stop time or a stop position in thetimeseries. Similarly, timeseries storage interface 316 can retrieveeventseries data from eventseries storage 329. Timeseries storageinterface 316 can also store timeseries data in timeseries storage 214and can store eventseries data in eventseries storage 329.Advantageously, timeseries storage interface 316 provides a consistentinterface which enables logical data independence.

In some embodiments, timeseries storage interface 316 stores timeseriesas lists of data samples, organized by time. For example, timeseriesstorage interface 316 can store timeseries in the following format:

[<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key, timestamp₃,value₃>]where key is an identifier of the source of the data samples (e.g.,timeseries ID, sensor ID, etc.), timestamp_(i) identifies a timeassociated with the ith sample, and value_(i) indicates the value of theith sample.

In some embodiments, timeseries storage interface 316 stores eventseriesas lists of events having a start time, an end time, and a state. Forexample, timeseries storage interface 316 can store eventseries in thefollowing format:

-   -   [<eventID₁, start_timestamp₁, end_timestamp₁, state₁>, . . . ,        <eventID_(N), start_timestamp_(N), end_timestamp_(N),        state_(N)>]        where eventID_(i) is an identifier of the ith event,        start_timestamp_(i) is the time at which the ith event started,        end_timestamp_(i) is the time at which the ith event ended,        state describes a state or condition associated with the ith        event (e.g., cold, hot, warm, etc.), and N is the total number        of events in the eventseries.

In some embodiments, timeseries storage interface 316 stores timeseriesand eventseries in a tabular format. Timeseries storage interface 316can store timeseries and eventseries in various tables having a columnfor each attribute of the timeseries/eventseries samples (e.g., key,timestamp, value). The timeseries tables can be stored in timeseriesstorage 214, whereas the eventseries tables can be stored in eventseriesstorage 329. In some embodiments, timeseries storage interface 316caches older data in timeseries storage 214 but stores newer data inRAM. This may improve read performance when the newer data are requestedfor processing.

In some embodiments, timeseries storage interface 316 omits one or moreof the attributes when storing the timeseries samples. For example,timeseries storage interface 316 may not need to repeatedly store thekey or timeseries ID for each sample in the timeseries. In someembodiments, timeseries storage interface 316 omits timestamps from oneor more of the samples. If samples of a particular timeseries havetimestamps at regular intervals (e.g., one sample each minute),timeseries storage interface 316 can organize the samples by timestampsand store the values of the samples in a row. The timestamp of the firstsample can be stored along with the interval between the timestamps.Timeseries storage interface 316 can determine the timestamp of anysample in the row based on the timestamp of the first sample and theposition of the sample in the row.

In some embodiments, timeseries storage interface 316 stores one or moresamples with an attribute indicating a change in value relative to theprevious sample value. The change in value can replace the actual valueof the sample when the sample is stored in timeseries storage 214. Thisallows timeseries storage interface 316 to use fewer bits when storingsamples and their corresponding values. Timeseries storage interface 316can determine the value of any sample based on the value of the firstsample and the change in value of each successive sample.

In some embodiments, timeseries storage interface 316 creates containersor data objects in which samples of timeseries data and/or eventseriesdata can be stored. The containers can be JSON objects or other types ofcontainers configured to store one or more timeseries samples and/oreventseries samples. Timeseries storage interface 316 can be configuredto add samples to the containers and read samples from the containers.For example, timeseries storage interface 316 can receive a set ofsamples from data collector 212, timeseries web service 302, eventsservice 303, and/or timeseries processing engine 304. Timeseries storageinterface 316 can add the set of samples to a container and send thecontainer to timeseries storage 214.

Timeseries storage interface 316 can use containers when reading samplesfrom timeseries storage 214. For example, timeseries storage interface316 can retrieve a set of samples from timeseries storage 214 and addthe samples to a container. In some embodiments, the set of samplesinclude all samples within a specified time period (e.g., samples withtimestamps in the specified time period) or eventseries samples having aspecified state. Timeseries storage interface 316 can provide thecontainer of samples to timeseries web service 302, events service 303,timeseries processing engine 304, applications 230, and/or othercomponents configured to use the timeseries/eventseries samples.

Still referring to FIG. 3, timeseries processing engine 304 is shown toinclude several timeseries operators 306. Timeseries operators 306 canbe configured to apply various operations, transformations, or functionsto one or more input timeseries to generate output timeseries and/oreventseries. The input timeseries can include raw timeseries data and/orderived timeseries data. Timeseries operators 306 can be configured tocalculate aggregate values, averages, or apply other mathematicaloperations to the input timeseries. In some embodiments, timeseriesoperators 306 generate virtual point timeseries by combining two or moreinput timeseries (e.g., adding the timeseries together), creatingmultiple output timeseries from a single input timeseries, or applyingmathematical operations to the input timeseries. In some embodiments,timeseries operators 306 perform data cleansing operations ordeduplication operations on an input timeseries. In some embodiments,timeseries operators 306 use the input timeseries to generateeventseries based on the values of the timeseries samples (described ingreater detail below). The output timeseries can be stored as derivedtimeseries data in timeseries storage 214. Similarly, the eventseriescan be stored as eventseries data in eventseries storage 329.

In some embodiments, timeseries operators 306 do not change or replacethe raw timeseries data, but rather generate various “views” of the rawtimeseries data. The views can be queried in the same manner as the rawtimeseries data. For example, samples can be read from the rawtimeseries data, transformed to create the view, and then provided as anoutput. Because the transformations used to create the views can becomputationally expensive, the views can be stored as “materializedviews” in timeseries storage 214. These materialized views are referredto as derived timeseries data throughout the present disclosure.

Timeseries operators 306 can be configured to run at query time (e.g.,when a request for derived timeseries data is received) or prior toquery time (e.g., when new raw data samples are received, in response toa defined event or trigger, etc.). This flexibility allows timeseriesoperators 306 to perform some or all of their operations ahead of timeand/or in response to a request for specific derived data timeseries.For example, timeseries operators 306 can be configured to pre-processone or more timeseries that are read frequently to ensure that thetimeseries are updated whenever new data samples are received. However,timeseries operators 306 can be configured to wait until query time toprocess one or more timeseries that are read infrequently to avoidperforming unnecessary processing operations.

In some embodiments, timeseries operators 306 are triggered in aparticular sequence defined by a directed acyclic graph (DAG). The DAGmay define a workflow or sequence of operations or transformations toapply to one or more input timeseries. For example, the DAG for a rawdata timeseries may include a data cleansing operation, an aggregationoperation, and a summation operation (e.g., adding two raw datatimeseries to create a virtual point timeseries). The DAGs can be storedin DAG storage 330, or internally within timeseries processing engine304. DAGs can be retrieved by workflow manager 322 and used to determinehow and when to process incoming data samples. Exemplary systems andmethods for creating and using DAGs are described in greater detailbelow.

Timeseries operators 306 can perform aggregations for dashboards,cleansing operations, logical operations for rules and fault detection,machine learning predictions or classifications, call out to externalservices, or any of a variety of other operations which can be appliedto timeseries data. The operations performed by timeseries operators 306are not limited to sensor data. Timeseries operators 306 can alsooperate on event data or function as a billing engine for a consumptionor tariff-based billing system.

Sample Aggregation

Still referring to FIG. 3, timeseries operators 306 are shown to includea sample aggregator 308. Sample aggregator 308 can be configured togenerate derived data rollups from the raw timeseries data. For eachdata point, sample aggregator 308 can aggregate a set of data valueshaving timestamps within a predetermined time interval (e.g., aquarter-hour, an hour, a day, etc.) to generate an aggregate data valuefor the predetermined time interval. For example, the raw timeseriesdata for a particular data point may have a relatively short interval(e.g., one minute) between consecutive samples of the data point. Sampleaggregator 308 can generate a data rollup from the raw timeseries databy aggregating all of the samples of the data point having timestampswithin a relatively longer interval (e.g., a quarter-hour) into a singleaggregated value that represents the longer interval.

For some types of timeseries, sample aggregator 308 performs theaggregation by averaging all of the samples of the data point havingtimestamps within the longer interval. Aggregation by averaging can beused to calculate aggregate values for timeseries of non-cumulativevariables such as measured value. For other types of timeseries, sampleaggregator 308 performs the aggregation by summing all of the samples ofthe data point having timestamps within the longer interval. Aggregationby summation can be used to calculate aggregate values for timeseries ofcumulative variables such as the number of faults detected since theprevious sample.

Referring now to FIGS. 4-5, a block diagram 400 and a data table 450illustrating an aggregation technique which can be used by sampleaggregator 308 is shown, according to some embodiments. In FIG. 4, adata point 402 is shown. Data point 402 is an example of a measured datapoint for which timeseries values can be obtained. For example, datapoint 402 is shown as an outdoor air temperature point and has valueswhich can be measured by a temperature sensor. Although a specific typeof data point 402 is shown in FIG. 4, it should be understood that datapoint 402 can be any type of measured or calculated data point.Timeseries values of data point 402 can be collected by data collector212 and assembled into a raw data timeseries 404.

As shown in FIG. 5, the raw data timeseries 404 includes a timeseries ofdata samples, each of which is shown as a separate row in data table450. Each sample of raw data timeseries 404 is shown to include atimestamp and a data value. The timestamps of raw data timeseries 404are ten minutes and one second apart, indicating that the samplinginterval of raw data timeseries 404 is ten minutes and one second. Forexample, the timestamp of the first data sample is shown as2015-12-31T23:10:00 indicating that the first data sample of raw datatimeseries 404 was collected at 11:10:00 PM on Dec. 31, 2015. Thetimestamp of the second data sample is shown as 2015-12-31T23:20:01indicating that the second data sample of raw data timeseries 404 wascollected at 11:20:01 PM on Dec. 31, 2015. In some embodiments, thetimestamps of raw data timeseries 404 are stored along with an offsetrelative to universal time, as previously described. The values of rawdata timeseries 404 start at a value of 10 and increase by 10 with eachsample. For example, the value of the second sample of raw datatimeseries 404 is 20, the value of the third sample of raw datatimeseries 404 is 30, etc.

In FIG. 4, several data rollup timeseries 406-414 are shown. Data rolluptimeseries 406-414 can be generated by sample aggregator 308 and storedas derived timeseries data. The data rollup timeseries 406-414 includean average quarter-hour timeseries 406, an average hourly timeseries408, an average daily timeseries 410, an average monthly timeseries 412,and an average yearly timeseries 414. Each of the data rollup timeseries406-414 is dependent upon a parent timeseries. In some embodiments, theparent timeseries for each of the data rollup timeseries 406-414 is thetimeseries with the next shortest duration between consecutivetimeseries values. For example, the parent timeseries for averagequarter-hour timeseries 406 is raw data timeseries 404. Similarly, theparent timeseries for average hourly timeseries 408 is averagequarter-hour timeseries 406; the parent timeseries for average dailytimeseries 410 is average hourly timeseries 408; the parent timeseriesfor average monthly timeseries 412 is average daily timeseries 410; andthe parent timeseries for average yearly timeseries 414 is averagemonthly timeseries 412.

Sample aggregator 308 can generate each of the data rollup timeseries406-414 from the timeseries values of the corresponding parenttimeseries. For example, sample aggregator 308 can generate averagequarter-hour timeseries 406 by aggregating all of the samples of datapoint 402 in raw data timeseries 404 that have timestamps within eachquarter-hour. Similarly, sample aggregator 308 can generate averagehourly timeseries 408 by aggregating all of the timeseries values ofaverage quarter-hour timeseries 406 that have timestamps within eachhour. Sample aggregator 308 can generate average daily timeseries 410 byaggregating all of the time series values of average hourly timeseries408 that have timestamps within each day. Sample aggregator 308 cangenerate average monthly timeseries 412 by aggregating all of the timeseries values of average daily timeseries 410 that have timestampswithin each month. Sample aggregator 308 can generate average yearlytimeseries 414 by aggregating all of the time series values of averagemonthly timeseries 412 that have timestamps within each year.

In some embodiments, the timestamps for each sample in the data rolluptimeseries 406-414 are the beginnings of the aggregation interval usedto calculate the value of the sample. For example, the first data sampleof average quarter-hour timeseries 406 is shown to include the timestamp2015-12-31T23:00:00. This timestamp indicates that the first data sampleof average quarter-hour timeseries 406 corresponds to an aggregationinterval that begins at 11:00:00 PM on Dec. 31, 2015. Since only onedata sample of raw data timeseries 404 occurs during this interval, thevalue of the first data sample of average quarter-hour timeseries 406 isthe average of a single data value (i.e., average(10)=10). The same istrue for the second data sample of average quarter-hour timeseries 406(i.e., average (20)=20).

The third data sample of average quarter-hour timeseries 406 is shown toinclude the timestamp 2015-12-31T23:30:00. This timestamp indicates thatthe third data sample of average quarter-hour timeseries 406 correspondsto an aggregation interval that begins at 11:30:00 PM on Dec. 31, 2015.Since each aggregation interval of average quarter-hour timeseries 406is a quarter-hour in duration, the end of the aggregation interval is11:45:00 PM on Dec. 31, 2015. This aggregation interval includes twodata samples of raw data timeseries 404 (i.e., the third raw data samplehaving a value of 30 and the fourth raw data sample having a value of40). Sample aggregator 308 can calculate the value of the third sampleof average quarter-hour timeseries 406 by averaging the values of thethird raw data sample and the fourth raw data sample (i.e., average(30,40)=35). Accordingly, the third sample of average quarter-hourtimeseries 406 has a value of 35. Sample aggregator 308 can calculatethe remaining values of average quarter-hour timeseries 406 in a similarmanner.

Still referring to FIG. 5, the first data sample of average hourlytimeseries 408 is shown to include the timestamp 2015-12-31T23:00:00.This timestamp indicates that the first data sample of average hourlytimeseries 408 corresponds to an aggregation interval that begins at11:00:00 PM on Dec. 31, 2015. Since each aggregation interval of averagehourly timeseries 408 is an hour in duration, the end of the aggregationinterval is 12:00:00 AM on Jan. 1, 2016. This aggregation intervalincludes the first four samples of average quarter-hour timeseries 406.Sample aggregator 308 can calculate the value of the first sample ofaverage hourly timeseries 408 by averaging the values of the first fourvalues of average quarter-hour timeseries 406 (i.e., average(10, 20, 35,50)=28.8). Accordingly, the first sample of average hourly timeseries408 has a value of 28.8. Sample aggregator 308 can calculate theremaining values of average hourly timeseries 408 in a similar manner.

The first data sample of average daily timeseries 410 is shown toinclude the timestamp 2015-12-31T00:00:00. This timestamp indicates thatthe first data sample of average daily timeseries 410 corresponds to anaggregation interval that begins at 12:00:00 AM on Dec. 31, 2015. Sinceeach aggregation interval of the average daily timeseries 410 is a dayin duration, the end of the aggregation interval is 12:00:00 AM on Jan.1, 2016. Only one data sample of average hourly timeseries 408 occursduring this interval. Accordingly, the value of the first data sample ofaverage daily timeseries 410 is the average of a single data value(i.e., average(28.8)=28.8). The same is true for the second data sampleof average daily timeseries 410 (i.e., average(87.5)=87.5).

In some embodiments, sample aggregator 308 stores each of the datarollup timeseries 406-414 in a single data table (e.g., data table 450)along with raw data timeseries 404. This allows applications 230 toretrieve all of the timeseries 404-414 quickly and efficiently byaccessing a single data table. In other embodiments, sample aggregator308 can store the various timeseries 404-414 in separate data tableswhich can be stored in the same data storage device (e.g., the samedatabase) or distributed across multiple data storage devices. In someembodiments, sample aggregator 308 stores data timeseries 404-414 in aformat other than a data table. For example, sample aggregator 308 canstore timeseries 404-414 as vectors, as a matrix, as a list, or usingany of a variety of other data storage formats.

In some embodiments, sample aggregator 308 automatically updates thedata rollup timeseries 406-414 each time a new raw data sample isreceived. Updating the data rollup timeseries 406-414 can includerecalculating the aggregated values based on the value and timestamp ofthe new raw data sample. When a new raw data sample is received, sampleaggregator 308 can determine whether the timestamp of the new raw datasample is within any of the aggregation intervals for the samples of thedata rollup timeseries 406-414. For example, if a new raw data sample isreceived with a timestamp of 2016-01-01T00:52:00, sample aggregator 308can determine that the new raw data sample occurs within the aggregationinterval beginning at timestamp 2016-01-01T00:45:00 for averagequarter-hour timeseries 406. Sample aggregator 308 can use the value ofthe new raw data point (e.g., value=120) to update the aggregated valueof the final data sample of average quarter-hour timeseries 406 (i.e.,average(110, 120)=115).

If the new raw data sample has a timestamp that does not occur withinany of the previous aggregation intervals, sample aggregator 308 cancreate a new data sample in average quarter-hour timeseries 406. The newdata sample in average quarter-hour timeseries 406 can have a new datatimestamp defining the beginning of an aggregation interval thatincludes the timestamp of the new raw data sample. For example, if thenew raw data sample has a timestamp of 2016-01-01T01:00:11, sampleaggregator 308 can determine that the new raw data sample does not occurwithin any of the aggregation intervals previously established foraverage quarter-hour timeseries 406. Sample aggregator 308 can generatea new data sample in average quarter-hour timeseries 406 with thetimestamp 2016-01-01T01:00:00 and can calculate the value of the newdata sample in average quarter-hour timeseries 406 based on the value ofthe new raw data sample, as previously described.

Sample aggregator 308 can update the values of the remaining data rolluptimeseries 408-414 in a similar manner. For example, sample aggregator308 determine whether the timestamp of the updated data sample inaverage quarter-hour timeseries is within any of the aggregationintervals for the samples of average hourly timeseries 408. Sampleaggregator 308 can determine that the timestamp 2016-01-01T00:45:00occurs within the aggregation interval beginning at timestamp2016-01-01T00:00:00 for average hourly timeseries 408. Sample aggregator308 can use the updated value of the final data sample of averagequarter-hour timeseries 406 (e.g., value=115) to update the value of thesecond sample of average hourly timeseries 408 (i.e., average(65, 80,95, 115)=88.75). Sample aggregator 308 can use the updated value of thefinal data sample of average hourly timeseries 408 to update the finalsample of average daily timeseries 410 using the same technique.

In some embodiments, sample aggregator 308 updates the aggregated datavalues of data rollup timeseries 406-414 each time a new raw data sampleis received. Updating each time a new raw data sample is receivedensures that the data rollup timeseries 406-414 always reflect the mostrecent data samples. In other embodiments, sample aggregator 308 updatesthe aggregated data values of data rollup timeseries 406-414periodically at predetermined update intervals (e.g., hourly, daily,etc.) using a batch update technique. Updating periodically can be moreefficient and require less data processing than updating each time a newdata sample is received, but can result in aggregated data values thatare not always updated to reflect the most recent data samples.

In some embodiments, sample aggregator 308 is configured to cleanse rawdata timeseries 404. Cleansing raw data timeseries 404 can includediscarding exceptionally high or low data. For example, sampleaggregator 308 can identify a minimum expected data value and a maximumexpected data value for raw data timeseries 404. Sample aggregator 308can discard data values outside this range as bad data. In someembodiments, the minimum and maximum expected values are based onattributes of the data point represented by the timeseries. For example,data point 402 represents a measured outdoor air temperature andtherefore has an expected value within a range of reasonable outdoor airtemperature values for a given geographic location (e.g., between −20°F. and 110° F.). Sample aggregator 308 can discard a data value of 330for data point 402 since a temperature value of 330° F. is notreasonable for a measured outdoor air temperature.

In some embodiments, sample aggregator 308 identifies a maximum rate atwhich a data point can change between consecutive data samples. Themaximum rate of change can be based on physical principles (e.g., heattransfer principles), weather patterns, or other parameters that limitthe maximum rate of change of a particular data point. For example, datapoint 402 represents a measured outdoor air temperature and thereforecan be constrained to have a rate of change less than a maximumreasonable rate of change for outdoor temperature (e.g., five degreesper minute). If two consecutive data samples of the raw data timeseries404 have values that would require the outdoor air temperature to changeat a rate in excess of the maximum expected rate of change, sampleaggregator 308 can discard one or both of the data samples as bad data.

Sample aggregator 308 can perform any of a variety of data cleansingoperations to identify and discard bad data samples. In someembodiments, sample aggregator 308 performs the data cleansingoperations for raw data timeseries 404 before generating the data rolluptimeseries 406-414. This ensures that raw data timeseries 404 used togenerate data rollup timeseries 406-414 does not include any bad datasamples. Accordingly, the data rollup timeseries 406-414 do not need tobe re-cleansed after the aggregation is performed.

Virtual Points

Referring again to FIG. 3, timeseries operators 306 are shown to includea virtual point calculator 310. Virtual point calculator 310 isconfigured to create virtual data points and calculate timeseries valuesfor the virtual data points. A virtual data point is a type ofcalculated data point derived from one or more actual data points. Insome embodiments, actual data points are measured data points, whereasvirtual data points are calculated data points. Virtual data points canbe used as substitutes for actual sensor data when the sensor datadesired for a particular application does not exist, but can becalculated from one or more actual data points. For example, a virtualdata point representing the enthalpy of a refrigerant can be calculatedusing actual data points measuring the temperature and pressure of therefrigerant. Virtual data points can also be used to provide timeseriesvalues for calculated quantities such as efficiency, coefficient ofperformance, and other variables that cannot be directly measured.

Virtual point calculator 310 can calculate virtual data points byapplying any of a variety of mathematical operations or functions toactual data points or other virtual data points. For example, virtualpoint calculator 310 can calculate a virtual data point (pointID₃) byadding two or more actual data points (pointID₁ and pointID₂) (e.g.,pointID₃=pointI+pointID₂). As another example, virtual point calculator310 can calculate an enthalpy data point (pointID₄) based on a measuredtemperature data point (pointID₅) and a measured pressure data point(pointID₆) (e.g., pointID₄=enthalpy(pointID₅, pointID₆)). In someinstances, a virtual data point can be derived from a single actual datapoint. For example, virtual point calculator 310 can calculate asaturation temperature (pointID₇) of a known refrigerant based on ameasured refrigerant pressure (pointID₈) (e.g.,pointID₇=T_(sat)(pointID₈)). In general, virtual point calculator 310can calculate the timeseries values of a virtual data point using thetimeseries values of one or more actual data points and/or thetimeseries values of one or more other virtual data points.

In some embodiments, virtual point calculator 310 uses a set of virtualpoint rules to calculate the virtual data points. The virtual pointrules can define one or more input data points (e.g., actual or virtualdata points) and the mathematical operations that should be applied tothe input data point(s) to calculate each virtual data point. Thevirtual point rules can be provided by a user, received from an externalsystem or device, and/or stored in memory 210. Virtual point calculator310 can apply the set of virtual point rules to the timeseries values ofthe input data points to calculate timeseries values for the virtualdata points. The timeseries values for the virtual data points can bestored as derived timeseries data in timeseries storage 214.

Virtual point calculator 310 can calculate virtual data points using thevalues of raw data timeseries 404 and/or the aggregated values of thedata rollup timeseries 406-414. In some embodiments, the input datapoints used to calculate a virtual data point are collected at differentsampling times and/or sampling rates. Accordingly, the samples of theinput data points may not be synchronized with each other, which canlead to ambiguity in which samples of the input data points should beused to calculate the virtual data point. Using the data rolluptimeseries 406-414 to calculate the virtual data points ensures that thetimestamps of the input data points are synchronized and eliminates anyambiguity in which data samples should be used.

Referring now to FIG. 6, several timeseries 600, 620, 640, and 660illustrating the synchronization of data samples resulting fromaggregating the raw timeseries data are shown, according to someembodiments. Timeseries 600 and 620 are raw data timeseries. Raw datatimeseries 600 has several raw data samples 602-610. Raw data sample 602is collected at time t₁; raw data sample 604 is collected at time t₂;raw data sample 606 is collected at time t₃; raw data sample 608 iscollected at time t₄; raw data sample 610 is collected at time t₅; andraw data sample 612 is collected at time t₆.

Raw data timeseries 620 also has several raw data samples 622, 624, 626,628, and 630. However, raw data samples, 622-630 are not synchronizedwith raw data samples 602-612. For example, raw data sample 622 iscollected before time t₁; raw data sample 624 is collected between timest₂ and t₃; raw data sample 626 is collected between times t₃ and t₄; rawdata sample 628 is collected between times t₄ and t₅; and raw datasample 630 is collected between times t₅ and t₆. The lack ofsynchronization between data samples 602-612 and raw data samples622-630 can lead to ambiguity in which of the data samples should beused together to calculate a virtual data point.

Timeseries 640 and 660 are data rollup timeseries. Data rolluptimeseries 640 can be generated by sample aggregator 308 by aggregatingraw data timeseries 600. Similarly, data rollup timeseries 660 can begenerated by sample aggregator 308 by aggregating raw data timeseries620. Both raw data timeseries 600 and 620 can be aggregated using thesame aggregation interval. Accordingly, the resulting data rolluptimeseries 640 and 660 have synchronized data samples. For example,aggregated data sample 642 is synchronized with aggregated data sample662 at time t_(1′). Similarly, aggregated data sample 644 issynchronized with aggregated data sample 664 at time t_(2′); aggregateddata sample 646 is synchronized with aggregated data sample 666 at timet_(3′); and aggregated data sample 648 is synchronized with aggregateddata sample 668 at time t_(4′).

The synchronization of data samples in data rollup timeseries 640 and660 allows virtual point calculator 310 to readily identify which of thedata samples should be used together to calculate a virtual point. Forexample, virtual point calculator 310 can identify which of the samplesof data rollup timeseries 640 and 660 have the same timestamp (e.g.,data samples 642 and 662, data samples 644 and 664, etc.). Virtual pointcalculator 310 can use two or more aggregated data samples with the sametimestamp to calculate a timeseries value of the virtual data point. Insome embodiments, virtual point calculator 310 assigns the sharedtimestamp of the input data samples to the timeseries value of thevirtual data point calculated from the input data samples.

Weather Points

Referring again to FIG. 3, timeseries operators 306 are shown to includea weather point calculator 312. Weather point calculator 312 isconfigured to perform weather-based calculations using the timeseriesdata. In some embodiments, weather point calculator 312 creates virtualdata points for weather-related variables such as cooling degree days(CDD), heating degree days (HDD), cooling energy days (CED), heatingenergy days (HED), and normalized energy consumption. The timeseriesvalues of the virtual data points calculated by weather point calculator312 can be stored as derived timeseries data in timeseries storage 214.

Weather point calculator 312 can calculate CDD by integrating thepositive temperature difference between the time-varying outdoor airtemperature T_(OA) and the cooling balance point T_(bC) as shown in thefollowing equation:

CDD=∫ ^(period)max{0,(T _(OA) −T _(bC))}dt

where period is the integration period. In some embodiments, the outdoorair temperature T_(OA) is a measured data point, whereas the coolingbalance point T_(bC) is a stored parameter. To calculate CDD for eachsample of the outdoor air temperature T_(OA), weather point calculator312 can multiply the quantity max{0, (T_(OA)−T_(bC))} by the samplingperiod Δt of the outdoor air temperature T_(OA). Weather pointcalculator 312 can calculate CED in a similar manner using outdoor airenthalpy E_(OA) instead of outdoor air temperature T_(OA). Outdoor airenthalpy E_(OA) can be a measured or virtual data point.

Weather point calculator 312 can calculate HDD by integrating thepositive temperature difference between a heating balance point T_(bH)and the time-varying outdoor air temperature T_(OA) as shown in thefollowing equation:

HDD=∫ ^(period)max{0,(T _(bH) −T _(OA))}dt

where period is the integration period. In some embodiments, the outdoorair temperature T_(OA) is a measured data point, whereas the heatingbalance point T_(bH) is a stored parameter. To calculate HDD for eachsample of the outdoor air temperature T_(OA), weather point calculator312 can multiply the quantity max{0, (T_(bH)−T_(OA))} by the samplingperiod Δt of the outdoor air temperature T_(OA). Weather pointcalculator 312 can calculate HED in a similar manner using outdoor airenthalpy E_(OA) instead of outdoor air temperature T_(OA).

In some embodiments, both virtual point calculator 310 and weather pointcalculator 312 calculate timeseries values of virtual data points.Weather point calculator 312 can calculate timeseries values of virtualdata points that depend on weather-related variables (e.g., outdoor airtemperature, outdoor air enthalpy, outdoor air humidity, outdoor lightintensity, precipitation, wind speed, etc.). Virtual point calculator310 can calculate timeseries values of virtual data points that dependon other types of variables (e.g., non-weather-related variables).Although only a few weather-related variables are described in detailhere, it is contemplated that weather point calculator 312 can calculatevirtual data points for any weather-related variable. Theweather-related data points used by weather point calculator 312 can bereceived as timeseries data from various weather sensors and/or from aweather service.

Fault Detection

Still referring to FIG. 3, timeseries operators 306 are shown to includea fault detector 314. Fault detector 314 can be configured to detectfaults in timeseries data. In some embodiments, fault detector 314performs fault detection for timeseries data representing meter data(e.g., measurements from a sensor) and/or for other types of timeseriesdata. Fault detector 314 can detect faults in the raw timeseries dataand/or the derived timeseries data. In some embodiments, fault detector314 receives fault detection rules from analytics service 224. Faultdetection rules can be defined by a user (e.g., via a rules editor) orreceived from an external system or device. In various embodiments, thefault detection rules can be stored within timeseries storage 214. Faultdetector 314 can retrieve the fault detection rules from timeseriesstorage 214 and can use the fault detection rules to analyze thetimeseries data.

In some embodiments, the fault detection rules provide criteria that canbe evaluated by fault detector 314 to detect faults in the timeseriesdata. For example, the fault detection rules can define a fault as adata value above or below a threshold value. As another example, thefault detection rules can define a fault as a data value outside apredetermined range of values. The threshold value and predeterminedrange of values can be based on the type of timeseries data (e.g., meterdata, calculated data, etc.), the type of variable represented by thetimeseries data (e.g., temperature, humidity, energy consumption, etc.),the system or device that measures or provides the timeseries data(e.g., a sensor, an IoT device, etc.), and/or other attributes of thetimeseries data.

Fault detector 314 can apply the fault detection rules to the timeseriesdata to determine whether each sample of the timeseries data qualifiesas a fault. In some embodiments, fault detector 314 generates a faultdetection timeseries containing the results of the fault detection. Thefault detection timeseries can include a set of timeseries values, eachof which corresponds to a data sample of the timeseries data evaluatedby fault detector 314. In some embodiments, each timeseries value in thefault detection timeseries includes a timestamp and a fault detectionvalue. The timestamp can be the same as the timestamp of thecorresponding data sample of the data timeseries. The fault detectionvalue can indicate whether the corresponding data sample of the datatimeseries qualifies as a fault. For example, the fault detection valuecan have a value of “Fault” if a fault is detected and a value of “Notin Fault” if a fault is not detected in the corresponding data sample ofthe data timeseries. The fault detection timeseries can be stored intimeseries storage 214 along with the raw timeseries data and thederived timeseries data.

Referring now to FIGS. 7-8, a block diagram and data table 800illustrating the fault detection timeseries is shown, according to someembodiments. In FIG. 7, fault detector 314 is shown receiving a datatimeseries 702 from timeseries storage 214. Data timeseries 702 can be araw data timeseries or a derived data timeseries. In some embodiments,data timeseries 702 is a timeseries of values of an actual data point(e.g., a measured temperature). In other embodiments, data timeseries702 is a timeseries of values of a virtual data point (e.g., acalculated efficiency). As shown in table 800, data timeseries 702includes a set of data samples. Each data sample includes a timestampand a value. Most of the data samples have values within the range of65-66. However, three of the data samples have values of 42.

Fault detector 314 can evaluate data timeseries 702 using a set of faultdetection rules to detect faults in data timeseries 702. In someembodiments, fault detector 314 determines that the data samples havingvalues of 42 qualify as faults according to the fault detection rules.Fault detector 314 can generate a fault detection timeseries 704containing the results of the fault detection. As shown in table 800,fault detection timeseries 704 includes a set of data samples. Like datatimeseries 702, each data sample of fault detection timeseries 704includes a timestamp and a value. Most of the values of fault detectiontimeseries 704 are shown as “Not in Fault,” indicating that no fault wasdetected for the corresponding sample of data timeseries 702 (i.e., thedata sample with the same timestamp). However, three of the data samplesin fault detection timeseries 704 have a value of “Fault,” indicatingthat the corresponding sample of data timeseries 702 qualifies as afault. As shown in FIG. 7, fault detector 314 can store fault detectiontimeseries 704 in timeseries storage 214 along with the raw timeseriesdata and the derived timeseries data.

Fault detection timeseries 704 can be used by web services platform 102to perform various fault detection, diagnostic, and/or controlprocesses. In some embodiments, fault detection timeseries 704 isfurther processed by timeseries processing engine 304 to generate newtimeseries derived from fault detection timeseries 704. For example,sample aggregator 308 can use fault detection timeseries 704 to generatea fault duration timeseries. Sample aggregator 308 can aggregatemultiple consecutive data samples of fault detection timeseries 704having the same data value into a single data sample. For example,sample aggregator 308 can aggregate the first two “Not in Fault” datasamples of fault detection timeseries 704 into a single data samplerepresenting a time period during which no fault was detected.Similarly, sample aggregator 308 can aggregate the final two “Fault”data samples of fault detection timeseries 704 into a single data samplerepresenting a time period during which a fault was detected.

In some embodiments, each data sample in the fault duration timeserieshas a fault occurrence time and a fault duration. The fault occurrencetime can be indicated by the timestamp of the data sample in the faultduration timeseries. Sample aggregator 308 can set the timestamp of eachdata sample in the fault duration timeseries equal to the timestamp ofthe first data sample in the series of data samples in fault detectiontimeseries 704 which were aggregated to form the aggregated data sample.For example, if sample aggregator 308 aggregates the first two “Not inFault” data samples of fault detection timeseries 704, sample aggregator308 can set the timestamp of the aggregated data sample to2015-12-31T23:10:00. Similarly, if sample aggregator 308 aggregates thefinal two “Fault” data samples of fault detection timeseries 704, sampleaggregator 308 can set the timestamp of the aggregated data sample to2015-12-31T23:50:00.

The fault duration can be indicated by the value of the data sample inthe fault duration timeseries. Sample aggregator 308 can set the valueof each data sample in the fault duration timeseries equal to theduration spanned by the consecutive data samples in fault detectiontimeseries 704 which were aggregated to form the aggregated data sample.Sample aggregator 308 can calculate the duration spanned by multipleconsecutive data samples by subtracting the timestamp of the first datasample of fault detection timeseries 704 included in the aggregationfrom the timestamp of the next data sample of fault detection timeseries704 after the data samples included in the aggregation.

For example, if sample aggregator 308 aggregates the first two “Not inFault” data samples of fault detection timeseries 704, sample aggregator308 can calculate the duration of the aggregated data sample bysubtracting the timestamp 2015-12-31T23:10:00 (i.e., the timestamp ofthe first “Not in Fault” sample) from the timestamp 2015-12-31T23:30:00(i.e., the timestamp of the first “Fault” sample after the consecutive“Not in Fault” samples) for an aggregated duration of twenty minutes.Similarly, if sample aggregator 308 aggregates the final two “Fault”data samples of fault detection timeseries 704, sample aggregator 308can calculate the duration of the aggregated data sample by subtractingthe timestamp 2015-12-31T23:50:00 (i.e., the timestamp of the first“Fault” sample included in the aggregation) from the timestamp2016-01-01T00:10:00 (i.e., the timestamp of the first “Not in Fault”sample after the consecutive “Fault” samples) for an aggregated durationof twenty minutes.

Eventseries

Referring again to FIG. 3, timeseries operators 306 are shown to includean eventseries generator 315. Eventseries generator 315 can beconfigured to generate eventseries based on the raw data timeseriesand/or the derived data timeseries. Each eventseries may include aplurality of event samples that characterize various events and definethe start times and end times of the events. In the context ofeventseries, an “event” can be defined as a state or condition thatoccurs over a period of time. In other words, an event is not aninstantaneous occurrence, but rather is a non-instantaneous state orcondition observed over a time period having a non-zero duration (i.e.,having both a start time and a subsequent stop time). The state orcondition of the event can be based on the values of the timeseriessamples used to generate the eventseries. In some embodiments,eventseries generator 315 assigns a state to each timeseries samplebased on the value of the timeseries sample and then aggregates multipleconsecutive samples having the same state to define the time period overwhich that state is observed.

Eventseries generator 315 can be configured to assign a state to eachsample of an input timeseries (e.g., a raw data timeseries or a derivedtimeseries) by applying a set of rules to each sample. The process ofassigning a state to each sample of the input timeseries can bedescribed as an event-condition-action (ECA) process. ECA refers to thestructure of active rules in event driven architecture and activedatabase systems. For example, each rule in the set of rules may includean event, a condition, and an action. The event part of the rule mayspecify a signal that triggers invocation of the rule. The conditionpart of the rule may be a logical test (or series of logical tests)that, if satisfied or evaluates to true, causes the action to be carriedout. The action part of the rule may specify one or more actions to beperformed when the corresponding logical test is satisfied (e.g.,assigning a particular state to a sample of the input timeseries).

In some embodiments, the event part is the arrival of a new sample of aninput timeseries. Different rules may apply to different inputtimeseries. For example, the arrival of a new sample of a first inputtimeseries may qualify as a first event, whereas the arrival of a newsample of a second input timeseries may qualify as a second event.Eventseries generator 315 can use the identity of the input timeseriesto determine which event has occurred when a new sample of a particularinput timeseries is received. In other words, eventseries generator 315can select a particular rule to evaluate based on the identity of theinput timeseries.

In some embodiments, the condition includes one or more mathematicalchecks or logic statements that are evaluated by eventseries generator315. For example, evaluating the condition of a particular rule mayinclude comparing the value of the sample of the input timeseries to athreshold value. The condition may be satisfied if the value of thesample is less than the threshold value, equal to the threshold value,or greater than the threshold value, depending on the particular logicstatement specified by the condition. In some embodiments, the conditionincludes a series of mathematical checks that are performed byeventseries generator 315 in a predetermined order. Each mathematicalcheck may correspond to a different action to be performed if thatmathematical check is satisfied. For example, the conditions andcorresponding actions may be specified as follows:

-   -   If Value >θ₁, Action=Action₁    -   Else If θ₁≥Value>θ₂, Action=Action₂    -   Else If θ₂≥Value>θ₃, Action=Action₃    -   Else If θ₃≥Value, Action=Action₄        where Value is the value of the sample of the input timeseries,        θ₁-θ₄ are thresholds for the value, and Action₁-Action₄ are        specific actions that are performed if the corresponding logic        statement is satisfied. For example, Action₁ may be performed if        the value of the sample is greater than θ₁.

In some embodiments, the actions include assigning various states to thesample of the input timeseries. For example, Action₁ may includeassigning a first state to the sample of the input timeseries, whereasAction₂ may include assigning a second state to the sample of the inputtimeseries. Accordingly, different states can be assigned to the samplebased on the value of the sample relative to the threshold values. Eachtime a new sample of an input timeseries is received, eventseriesgenerator 315 can run through the set of rules, select the rules thatapply to that specific input timeseries, apply them in a predeterminedorder, determine which condition is satisfied, and assign a particularstate to the sample based on which condition is satisfied.

One example of an eventseries which can be generated by eventseriesgenerator 315 is an outdoor air temperature (OAT) eventseries. The OATeventseries may define one or more temperature states and may indicatethe time periods during which each of the temperature states isobserved. In some embodiments, the OAT eventseries is based on atimeseries of measurements of the OAT received as a raw data timeseries.Eventseries generator 315 can use a set of rules to assign a particulartemperature state (e.g., hot, warm, cool, cold) to each of thetimeseries OAT samples. For example, eventseries generator 315 can applythe following set of rules to the samples of an OAT timeseries:

-   -   If OAT>100,State=Hot    -   Else If 100≥OAT>80, State=Warm    -   Else If 80≥OAT>50,State=Cool    -   Else If 50≥OAT, State=Cold        where OAT is the value of a particular timeseries data sample.        If the OAT is above 100, eventseries generator 315 can assign        the timeseries sample to the “Hot” temperature state. If the OAT        is less than or equal to 100 and greater than 80, eventseries        generator 315 can assign the timeseries sample to the “Warm”        temperature state. If the OAT is less than or equal to 80 and        greater than 50, eventseries generator 315 can assign the        timeseries sample to the “Cool” temperature state. If the OAT is        less than or equal to 50, eventseries generator 315 can assign        the timeseries sample to the “Cold” temperature state.

In some embodiments, eventseries generator 315 creates a new timeseriesthat includes the assigned states for each sample of the original inputtimeseries. The new timeseries may be referred to as a “statetimeseries” because it indicates the state assigned to each timeseriessample. The state timeseries can be created by applying the set of rulesto an input timeseries as previously described. In some embodiments, thestate timeseries includes a state value and a timestamp for each sampleof the state timeseries. An example of a state timeseries is as follows:[

state₁, timestamp₁

,

state₂, timestamp₂

, . . .

state_(N), timestamp_(N)

] where state_(i) is the state assigned to the ith sample of the inputtimeseries, timestamp is the timestamp of the ith sample of the inputtimeseries, and N is the total number of samples in the inputtimeseries. In some instances, two or more of the state values may bethe same if the same state is assigned to multiple samples of the inputtimeseries.

In some embodiments, the state timeseries also includes the originalvalue of each sample of the input timeseries. For example, each sampleof the state timeseries may include a state value, a timestamp, and aninput data value, as shown in the following equation:

[

state₁,timestamp₁,value₁

, . . .

state_(N),timestamp_(N),value_(N)

]

where value_(i) is the original value of the ith sample of the inputtimeseries. The state timeseries is a type of derived timeseries whichcan be stored and processed by timeseries service 228.

Referring now to FIG. 9A, a table 910 illustrating the result ofassigning a temperature state to each timeseries sample is shown,according to some embodiments. Each timeseries sample is shown as aseparate row of table 910. The “Time” column of table 910 indicates thetimestamp associated with each sample, whereas the “OAT” column of table910 indicates the value of each timeseries sample. The “State” column oftable 910 indicates the state assigned to each timeseries sample byeventseries generator 315.

Referring now to FIG. 9B, a table 920 illustrating a set of eventsgenerated by eventseries generator 315 is shown, according to someembodiments. Each event is shown as a separate row of table 920. The“Event ID” column of table 920 indicates the unique identifier for eachevent (e.g., Event 1, Event 2, etc.). The “Start Time” column of table920 indicates the time at which each event begins and the “End Time”column of table 920 indicates the time at which event ends. The “State”column of table 920 indicates the state associated with each event.

Eventseries generator 315 can generate each event shown in table 920 byidentifying consecutive timeseries samples with the same assigned stateand determining a time period that includes the identified samples. Insome embodiments, the time period starts at the timestamp of the firstsample having a given state and ends immediately before the timestamp ofthe next sample having a different state. For example, the first twotimeseries samples shown in table 910 both have the state “Cold,”whereas the third sample in table 910 has the state “Cool.” Eventseriesgenerator 315 can identify the first two samples as having the samestate and can generate the time period 00:00-01:59 which includes bothof the identified samples. This time period begins at the timestamp ofthe first sample (i.e., 00:00) and ends immediately before the timestampof the third sample (i.e., 02:00). Eventseries generator 315 can createan event for each group of consecutive samples having the same state.

Eventseries generator 315 can perform a similar analysis for theremaining timeseries samples in table 910 to generate each of the eventsshown in table 920. In some instances, multiple events can have the samestate associated therewith. For example, both Event 1 and Event 7 shownin table 920 have the “Cold” state. Similarly, both Event 2 and Event 6have the “Cool” state and both Event 3 and Event 5 have the “Warm”state. It should be noted that an event defines not only a particularstate, but also a time period (i.e., a series of consecutive timesamples) during which that state is observed. If the same state isobserved during multiple non-consecutive time periods, multiple eventshaving the same state can be generated to represent each of thenon-consecutive time periods.

In some embodiments, eventseries generator 315 creates an eventseriesfor a set of events. An eventseries is conceptually similar to atimeseries in that both represent a series of occurrences. However, thesamples of a timeseries correspond to instantaneous occurrences having asingle timestamp, whereas the samples of an eventseries correspond tonon-instantaneous events having both a start time and a stop time. Forexample, eventseries generator 315 may create the following eventseriesfor the set of events shown in table 920:

-   -   [        ID=1, State=Cold, StartTime=00:00, EndTime=01; 59        ,        -   ID=2, State=Cool, StartTime=02:00, EndTime=08; 59            ,        -   ID=3, State=Warm, StartTime=09:00, EndTime=11; 59            ,        -   ID=4, State=Hot, StartTime=12:00, EndTime=15; 59            ,        -   ID=5, State=Warm, StartTime=16:00, EndTime=18; 59            ,        -   ID=6, State=Cool, StartTime=19:00, EndTime=21; 59            ,        -   ID=7, State=Cold, StartTime=22:00, EndTime=23; 59            ]            where each item within the bent brackets            is an event having the attributes ID, State, StartTime, and            EndTime. Events can be stored in a tabular format (as shown            in FIG. 9B), as a text string (as shown above), as a data            object (e.g., a JSON object), in a container format, or any            of a variety of formats.

Eventseries Process

Referring now to FIG. 9C, a flowchart of a process 960 for creating andupdating eventseries is shown, according to some embodiments. Process960 can be performed by eventseries generator 315, as described withreference to FIGS. 3 and 7-8. In some embodiments, process 960 isperformed to create an eventseries based on the samples of a datatimeseries. Process 960 can be performed after all of the samples of thedata timeseries have been collected or can be performed each time a newsample of the data timeseries is collected.

Process 960 is shown to include obtaining a new sample of a datatimeseries (step 962) and assigning a state to the sample using a set ofrules (step 964). In some embodiments, the sample is obtained from asensor configured to measure a variable of interest. For example, thesample can be a sample of a raw data timeseries. In other embodiments,the sample is a sample of a derived data timeseries generated by sampleaggregator 308, virtual point calculator 310, weather point calculator312, or other timeseries operators 306. The sample can be obtained froma set of samples of a complete timeseries or can be received as thelatest sample of an incoming data stream.

In some embodiments, step 964 includes applying a set of rules to thesample of the data timeseries to determine which state to assign. Theset of rules may define various ranges of values and a correspondingstate for each range of values. Step 964 can include assigning thesample to a particular state if the value of the value of the sample iswithin the corresponding range of values. For example, if the sample isa sample of outdoor air temperature (OAT), the set of rules may definevarious temperature ranges and a temperature state for each of thetemperature ranges. One example of such a set of rules is as follows:

-   -   If OAT>100,State=Hot    -   Else If 100≥OAT>80, State=Warm    -   Else If 80≥OAT>50,State=Cool    -   Else If 50≥OAT, State=Cold        where OAT is the value of a particular timeseries data sample.        If the OAT is above 100, the sample can be assigned to the “Hot”        temperature state. If the OAT is less than or equal to 100 and        greater than 80, the sample can be assigned to the “Warm”        temperature state. If the OAT is less than or equal to 80 and        greater than 50, the sample can be assigned to the “Cool”        temperature state. If the OAT is less than or equal to 50, the        sample can be assigned to the “Cold” temperature state.

Still referring to FIG. 9C, process 960 is shown to include determiningwhether the sample is part of an existing event (step 966). Step 966 mayinclude identifying all of the events in an existing eventseries anddetermining whether the sample belongs to any of the identified events.Each event may be defined by the combination of a particular state and atime period having both a start time and an end time. Step 966 mayinclude determining that the sample is part of an existing event if thesample is both (1) assigned to the same state as the existing event and(2) has a timestamp that is either (a) within the time period associatedwith the existing event or (b) consecutive with the time periodassociated with the existing event. However, step 966 may includedetermining that the sample is not part of an existing event if thesample does not have the same state as the existing event or does nothave a timestamp that that is either within the time period associatedwith the existing event or consecutive with the time period associatedwith the existing event.

In step 966, a timestamp may be considered within the time periodassociated with an existing event if the timestamp is between the starttime of the event and the end time of the event. A timestamp may beconsidered consecutive with the time period associated with an existingevent if the timestamp is immediately before the start time orimmediately after the end time of the event. For example, if a newsample has a timestamp before the start time of an event and no othersamples have intervening timestamps between the timestamp of the newsample and the start time of the event, the timestamp may be consideredconsecutive with the time period associated with the existing event.Similarly, if a new sample has a timestamp after the end time of anevent and no other samples have intervening timestamps between the endtime of the event and the timestamp of the new sample, the timestamp maybe considered consecutive with the time period associated with theexisting event.

If the new sample is part of an existing event (i.e., the result of step966 is “yes”), process 960 may proceed to determining whether the newsample extends the existing event (step 968). Step 968 may includedetermining whether the timestamp of the new sample is consecutive withthe time period associated with the existing event (i.e., immediatelybefore the start time of the event or immediately after the end time ofthe event). If the timestamp of the new sample is consecutive with thetime period associated with the existing event, step 968 may includedetermining that the sample extends the existing event. However, if thetimestamp of the new sample is not consecutive with the time periodassociated with the existing event, step 968 may include determiningthat the sample does not extend the existing event.

If the sample does not extend the existing event (i.e., the result ofstep 968 is “no”), process 960 may include determining that no update tothe existing event is needed. This situation may occur when thetimestamp of the new sample is between the start time of the existingevent and the end time of the existing event (i.e., within the timeperiod associated with the existing event). Since the time periodassociated with the existing event already covers the timestamp of thenew sample, it may be unnecessary to update the existing event toinclude the timestamp of the new sample.

However, if the sample extends the existing event (i.e., the result ofstep 968 is “yes”), process 960 may proceed to updating the start timeor end time of the existing event based on the timestamp of the sample(step 972). Step 972 may include moving the start time of the eventbackward in time or moving the end time of the event forward in timesuch that the time period between the start time and the end timeincludes the timestamp of the new sample. For example, if the timestampof the sample is before the start time of the event, step 972 mayinclude replacing the start time of the existing event with thetimestamp of the sample.

Similarly, if the timestamp of the sample is after the end time of theevent, step 972 may include replacing the end time of the existing eventwith a new end time that occurs after the timestamp of the sample. Forexample, if the existing event has an original end time of 04:59 and thenew sample has a timestamp of 05:00, step 972 may include updating theend time of the event to 05:59 (or any other time that occurs after05:00) such that the adjusted time period associated with the eventincludes the timestamp of the new sample. If the original end time ofthe existing event is “Null” and the new sample extends the end time ofthe existing event, step 972 may maintain the original end time of“Null.”

Returning to step 966, if the sample is not part of an existing event(i.e., the result of step 966 is “no”), process 960 may proceed tocreating a new event based on the state and the timestamp of the newsample (step 974). The new event may have a state that matches the stateassigned to the new sample in step 964. The new event may have a starttime equal to the timestamp of the sample and an end time that occursafter the timestamp of the sample such that the time period associatedwith the new event includes the timestamp of the sample. The end timemay have a value of “Null” if the new event is the last event in theeventseries or a non-null value of the new event is not the last eventin the eventseries. For example, if the next event in the timeseriesbegins at timestamp 06:00, step 974 may include setting the end time ofthe new event to 05:59.

After creating the new event in step 974, process 960 may perform steps976-988 to update other events in the eventseries based on the newinformation provided by the new event. For example, if the new event isthe last event in the eventseries (i.e., the result of step 976 is“yes”), process 960 may update the end time of the previous event (i.e.,the event that occurs immediately before the new event) (step 978). Theupdate performed in step 978 may include setting the end time of theprevious event to a time immediately before the timestamp of the newsample. For example, if the new sample has a timestamp of 05:00, step978 may include updating the end time of the previous event to 04:59. Ifthe new event is not the last event in the eventseries (i.e., the resultof step 976 is “no”), process 960 may proceed to step 980.

If the new event occurs between existing events in the eventseries(i.e., the result of step 980 is “yes”), process 960 may update the endtime of the previous event (step 982). The update performed in step 982may be the same as the update performed in step 978. For example, theupdate performed in step 982 may include setting the end time of theprevious event to a time immediately before the timestamp of the newsample. If the new event does not occur between existing events in theeventseries (i.e., the result of step 980 is “no”), process 960 mayproceed to step 984.

If the new event splits an existing event in the eventseries (i.e., theresult of step 984 is “yes”), process 960 may split the existing eventinto two events with the new event in between. In some embodiments,splitting the existing event into two events includes updating the endtime of the existing event to end before the new event (step 986) andcreating a second new event beginning after the first new event andending at the previous end time of the existing event (step 988). Forexample, consider a situation in which the existing event has a starttime of 04:00, an end time of 11:59, and a state of “Warm.” The newevent added in step 974 may have a start time of 08:00, an end time of08:59, and a state of “Hot.” Accordingly, step 986 may include changingthe end time of the existing event to 07:59 such that the existing eventcorresponds to a first “Warm” event and covers the time period from04:00 to 07:59. The intervening “Hot” event may cover the time periodfrom 08:00 to 08:59. The second new event created in step 988 (i.e., thesecond “Warm” event) may have a start time of 09:00 and an end time of11:59. The state of the second new event may be the same as the state ofthe existing event.

Properties of Events and Eventseries

Similar to timeseries, an eventseries can be used in two ways. In someembodiments, an event series is used for storage only. For example,events can be created by an external application and stored in aneventseries. In this scenario, the eventseries is used only as a storagecontainer. In other embodiments, eventseries can be used for bothstorage and processing. For example, events can be created byeventseries generator 315 based on raw or derived timeseries by applyinga set of rules, as previously described. In this scenario, theeventseries is both the storage container and the mechanism for creatingthe events.

In some embodiments, each eventseries includes the following propertiesor attributes: EventseriesID, OrgID, InputTimeseriesID,StateTimeseriesID, Rules, and Status. The EventseriesID property may bea unique ID generated by eventseries generator 315 when a neweventseries is created. The EventseriesID property can be used touniquely identify the eventseries and distinguish the eventseries fromother eventseries. The OrgID property may identify the organization(e.g., “ABC Corporation”) to which the eventseries belongs. Similar totimeseries, each eventseries may belong to a particular organization,customer, facility, or other entity (described in greater detail withreference to FIGS. 11A-11B).

The InputTimeseriesID property may identify the timeseries used tocreate the eventseries. For example, if the eventseries is a series ofoutdoor air temperature (OAT) events, the InputTimeseriesID property mayidentify the OAT timeseries from which the OAT eventseries is generated.In some embodiments, the input timeseries has the following format:

-   -   [<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key,        timestamp₃, value₃>]        where key is an identifier of the source of the data samples        (e.g., timeseries ID, sensor ID, etc.), timestamp_(i) identifies        a time associated with the ith sample, and value_(i) indicates        the value of the ith sample.

The Rules property may identify a list of rules that are applied to theinput timeseries to assign a particular state to each sample of theinput timeseries. In some embodiments, the list of rules includes aplurality of rules that are applied in a particular order. The order maybe defined by the logical structure of the rules. For example, the rulesmay include a set of “If” and “Elself” statements that are evaluated inthe order in which the statements appear in the set of rules. An exampleof a set of rules is as follows:

-   -   If OAT>100, State=Hot    -   Else If 100≥OAT>80, State=Warm    -   Else If 80≥OAT>50, State=Cool    -   Else If 50≥OAT, State=Cold

The StateTimeseriesID property may identify the state timeseries inwhich the assigned states are stored. The state timeseries can becreated by applying the set of rules to an input timeseries aspreviously described. In some embodiments, the state timeseries includesa state value and a timestamp for each sample of the state timeseries.An example of a state timeseries is as follows:

-   -   [        state₁, timestamp₁        ,        state₂, timestamp₂        , . . .        state_(N), timestamp_(N)        ]        where state_(i) is the state assigned to the ith sample of the        input timeseries, timestamp_(i) is the timestamp of the ith        sample of the input timeseries, and N is the total number of        samples in the input timeseries.

The Status property may indicate whether the eventseries is active(i.e., Status=Active) or inactive (i.e., Status=Inactive). In someembodiments, an eventseries is active by default when the eventseries iscreated. An eventseries can be deactivated by events service 303. Eventsservice 303 can change the Status property from active to inactive upondeactivating an eventseries.

Each eventseries may include a set of events. Each event may include thefollowing properties: EventID, State, StartTimestamp, EndTimestamp, andEventseriesID. The EventID property may be a unique ID generated byeventseries generator 315 when a new event is created. The EventIDproperty can be used to uniquely identify a particular event anddistinguish the event from other events in the eventseries. The Stateproperty may be a text string that defines the state associated with theevent. Each event may be uniquely associated with one state. TheStartTimestamp property may indicate the start time of the event,whereas the EndTimestamp property may indicate the end time of theevent. The StartTimestamp and EndTimestamp properties may be timestampsin any of a variety of formats (e.g., 2017-01-01T00:00:00). TheEventseriesID property may identify the eventseries which includes theevent. The EventseriesID property may be the same unique identifier usedto identify and distinguish eventseries from each other.

Event Service

Referring again to FIG. 3, timeseries service 228 is shown to include anevent service 303. In some embodiments, event service 303 is part oftimeseries service 228. In other embodiments, event service 303 is aseparate service (i.e., separate from timeseries service 228) withinplatform services 220. Event service 303 can be configured to receiveand process requests for information relating to various events andeventseries. Event service 303 can also create and update events andeventseries in response to a request from an application or a user.Several examples of how event service 303 can handle requests aredescribed below. The following table identifies the types of actionsevent service 303 can perform with respect to events and eventseries:

Resource GET (read) POST (create) PUT (update) /Eventseries Retrievelist of Create one or more N/A Eventseries new Eventseries/Eventseries/{eventseriesId} Read a specific Create a specific Updatethe specific Eventseries Eventseries Eventseries /Events Retrieve a listof Create one or more N/A Events new Events /Events/{eventId} Read aspecific Create a specific Update the specific Event Event Event

Event service 303 can be configured to create a new eventseries inresponse to a request containing an OrgID attribute and a processingtype attribute. For example, event service 303 can receive the followingrequest:

  Post {timeseriesV2}/eventseries/new {  “orgId”: “Abc Inc”, “ProcessingType” : “none” }

where “Abc Inc” is the ID of the organization to which the neweventseries will belong and no processing type is specified.

In response to this request, event service 303 can create a neweventseries (i.e., an empty eventseries container) and assign anEventseriesID to the eventseries. For example, event service 303 canrespond to the request as follows:

  {  “eventseriesId”: “c7c157e4-303f-4b25-b182-ce7b0f8291d8”,  “orgId”:“Abc Inc”,  “inputTimeseriesId”: null,  “stateTimeseriesId”: null, “rules”: null,  “status”: “active”,  “processingType”: “stream” }

In some embodiments, event service 303 is configured to create a neweventseries in response to a request containing an OrgID attribute, anInputTimeseriesID attribute, a StateTimeseriesID attribute, and a Rulesattribute. For example, event service 303 can receive the followingrequest:

  {  “orgId”: “Abc Inc”,  “inputTimeseriesId”: “793c156e4-303f-4b2e-bt82-ce7b0M9uj3”,  “stateTimeseriesId”:“uic157e4-6r2f-4b25-b682-ct7b0M917u”,  “rules”:   {“compareOp”: “Gt”,“scalar”: 100, “state”: “Hot”},   {“compareOp”: “Gt”, “scalar”: 80,“state”: “Warm”},   {“compareOp”: “Gt”, “scalar”: 50, “state”: “Cool”},  {“comp areOp”: “Lte”, “scalar”: 50, “state”: “Cold”}  ] }where “793c156e4-303f-4b2e-bt82-ce7b0f829uj3” is the ID of the inputtimeseries used to generate the eventseries,“uic157e4-6r2f-4b25-b682-ct7b0f82917u” is the ID of the state timeseriescontaining the states assigned to each sample of the input timeseries,and the “rules” attribute contains a set of rules used to assign a stateto each sample of the input timeseries.

In response to this request, event service 303 can create a neweventseries (i.e., an empty eventseries container) and assign anEventseriesID to the eventseries. For example, event service 303 canrespond to the request as follows:

  {  “eventseriesId”: “c7c157e4-303f-4b25-b182-ce7b0f8291d8”,  “orgId”:“Abc Inc”,  “inputTimeseriesId”: “793 c156e4-303f-4b2e-bt82-ce7b0M9uj3”, “stateTimeseriesId”: “uic157e4-6r2f-4b25-b682-ct7b0M917u”,  “rules”: [  {“comp areOp”: “Gt”, “scalar”: 100, “state”: “Hot”},   {“comp areOp”:“Gt”, “scalar”: 80, “state”: “Warm”},   {“compareOp”: “Gt”, “scalar”:50, “state”: “Cool”},   {“comp areOp”: “Lte”, “scalar”: 50, “state”:“Cold”}  ],  “status”: “active”,  “processingType”: “stream” }

In some embodiments, event service 303 is configured to add new eventsto an existing eventseries. For example, event service 303 can receive arequest to add a new event to an eventseries. The request may specifythe EventseriesID, the start time of the event, the end time of theevent, and the state associated with the event, as shown in thefollowing request:

  Post {timeseriesV2}/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events [  {   “eventseriesId”:“c7c157e4-303f-4b25-b182-ce7b0f8291d8”,   “startTimestamp”: “2017-04-0113:48:23-05:00”,   “endTimestamp”: “2017-04-01 13:54:11-05:00”,  “state”: “High Pressure Alarm”  } ]

In response to this request, event service 303 can generate a newEventID for the new event and can add the new event to the eventseriesdesignated by the EventseriesID “c7c157e4-303f-4b25-b182-ce7b0f8291d8.”The new event may have the start time “2017-04-01 13:48:23-05:00,” theend time “2017-04-01 13:54:11-05:00,” and the state “High PressureAlarm” as specified in the request. In some embodiments, event service303 responds to the request by acknowledging that the new event has beenadded to the eventseries.

In some embodiments, event service 303 is configured to update existingevents in an eventseries. For example, event service 303 can receive arequest to add update one or more properties of an existing event in aneventseries. The request may specify the EventseriesID, the updatedstart time of the event, the updated end time of the event, and/or theupdated state associated with the event, as shown in the followingrequest:

  Put {timeseriesV2}/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events/c7c157e4-303f-4b25-b182-ce7b0f8291d8 { “eventseriesId”: “c7c157e4-303f-4b25-b182-ce7b0f8291d8”, “startTimestamp”: “2017-04-01 13:48:23-05:00”,  “endTimestamp”:“2017-04-01 13:54:11-05:00”,  “state”: “High Pressure Alarm” }

In response to this request, event service 303 can update the specifiedproperties of the event designated by EventseriesID“c7c157e4-303f-4b25-b182-ce7b0f8291d8.” The updated event may have thestart time “2017-04-01 13:48:23-05:00,” the end time “2017-04-0113:54:11-05:00,” and the state “High Pressure Alarm” as specified in therequest. In some embodiments, event service 303 responds to the requestby acknowledging that the event has been updated.

In some embodiments, event service 303 is configured to read the eventsof an eventseries. For example, event service 303 can receive a requestto identify all of the events associated with an eventseries. Therequest may be specified as a get request as follows:

-   -   Get        {timeseriesV2}/eventseries/c7c157e4-303f-4b25-b182-ce7b0M91d8/events        where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the        EventseriesID of a specific eventseries.

In response to this request, event service 303 can search for all eventsof the specified eventseries and can return a list of the identifiedevents. An example response which can be provided by event service 303is as follows:

  [  {   “eventid”: “g9c197e4-003f-4u25-b182-se7b0f1945y”,  “eventseriesId”: “c7c157e4-303f-4b25-b182-ce7b0f8291d8”,  “startTimestamp”: “2017-04-01 13:48:23-05:00”,   “endTimestamp”:“2017-04-01 13:54:11-05:00”,   “state”: “High Pressure Alarm”  } ]where “g9c197e4-003f-4u25-b182-se7b0f81945y” is the EventID of anidentified event matching the search parameters. The response mayspecify the EventseriesID, StartTimestamp, EndTimestamp, and Stateproperties of each identified event.

In some embodiments, event service 303 is configured to search for theevents of an eventseries that have a specific state. For example, eventservice 303 can receive a request to identify all of the eventsassociated with a particular eventseries which have a specific state.The request may be specified as a get request as follows:

  Get {timeseriesV2}/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events?state=Hotwhere “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the EventseriesID of aparticular eventseries and “state=Hot” specifies that the search shouldreturn only events of the eventseries that have the state “Hot.” Inresponse to this request, event service 303 may search for all matchingevents (i.e., events of the specified eventseries that have thespecified state) and may return a list of events that match the searchparameters.

In some embodiments, event service 303 is configured to search for theevents of an eventseries that have a start time or end time matching agiven value. For example, event service 303 can receive a request toidentify all of the events of a particular eventseries that have a starttime or end time that matches a specified timestamp. The request may bespecified as a get request as follows:

  Get {timeseriesV2 }/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events?startTime=2017-04-01%2010:00:00-05:00&endTime=2017-04-01%2010:00:00-05:00where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the EventseriesID of aparticular eventseries and the “startTime” and “endTime” parametersspecify the start time and end time of the event. In response to thisrequest, event service 303 may search for all matching events (i.e.,(startTimestamp of event<startTime and endTimestamp of event>endTime)and may return a list of events that match the search parameters.

In some embodiments, event service 303 is configured to search for theevents of an eventseries that have a time range overlapping (at leastpartially) with a specified time range. For example, event service 303can receive a request to identify all of the events of a particulareventseries that have (1) an event start time before a specified starttime and an event end time after the specified start time or (2) anevent start time before a specified end time and an event end time afterthe specified end time. The request may be specified as a get request asfollows:

  Get {timeseriesV2 }/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events?startTime=2017-04-01%2010:00:00-05:00&endTime=2017-04-01%2011:59:00-05:00where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the EventseriesID of aparticular eventseries and the “startTime” and “endTime” parametersspecify the start time and end time of the event. In response to thisrequest, event service 303 may search for all events that match thefollowing criteria:

[(startTimestamp of event < startTime of query) AND (endTimestamp ofevent > startTime of query)] OR [(startTimestamp of event < endTime ofquery) AND (endTimestamp of event > endTime of query)]and may return a list of events that match these criteria.

In some embodiments, event service 303 is configured to search forevents of an eventseries that have a specific state and a time rangethat overlaps (at least partially) with a given time range. For example,event service 303 can receive a request to identify all of the events ofa particular eventseries that have a particular state and either (1) anevent start time before a specified start time and an event end timeafter the specified start time or (2) an event start time before aspecified end time and an event end time after the specified end time.The request may be specified as a get request as follows:

  Get {timeseriesV2}/eventseries/c7c157e4-303f-4b25-b182-ce7b0f8291d8/events?state=Hot&startTime=2017-04-01%2010:00:00-05:00&endTime=2017-04-01%2011:59:00-05:00where “c7c157e4-303f-4b25-b182-ce7b0f8291d8” is the EventseriesID of aparticular eventseries, the “state” parameter specifies a particularstate, and the “startTime” and “endTime” parameters specify the starttime and end time of the event. In response to this request, eventservice 303 may search for all events that match the following criteria:

State=Hot AND [(startTimestamp of event < startTime of query) AND(endTimestamp of event > startTime of query)] OR [(startTimestamp ofevent < endTime of query) AND (endTimestamp of event > endTime ofquery)]and may return a list of events that match these criteria.

Directed Acyclic Graphs

Referring again to FIG. 3, timeseries processing engine 304 is shown toinclude a directed acyclic graph (DAG) generator 320. DAG generator 320can be configured to generate one or more DAGs for each raw datatimeseries. Each DAG may define a workflow or sequence of operationswhich can be performed by timeseries operators 306 on the raw datatimeseries. When new samples of the raw data timeseries are received,workflow manager 322 can retrieve the corresponding DAG and use the DAGto determine how the raw data timeseries should be processed. In someembodiments, the DAGs are declarative views which represent the sequenceof operations applied to each raw data timeseries. The DAGs may bedesigned for timeseries rather than structured query language (SQL). Insome embodiments, each DAG (i.e., each timeseries processing workflow)applies to one or more input timeseries and is triggered when a newsample of any of the one or more input timeseries is received.

In some embodiments, DAGs apply over windows of time. For example, thetimeseries processing operations defined by a DAG may include a dataaggregation operation that aggregates a plurality of raw data sampleshaving timestamps within a given time window. The start time and endtime of the time window may be defined by the DAG and the timeseries towhich the DAG is applied. The DAG may define the duration of the timewindow over which the data aggregation operation will be performed. Forexample, the DAG may define the aggregation operation as an hourlyaggregation (i.e., to produce an hourly data rollup timeseries), a dailyaggregation (i.e., to produce a daily data rollup timeseries), a weeklyaggregation (i.e., to produce a weekly data rollup timeseries), or anyother aggregation duration. The position of the time window (e.g., aspecific day, a specific week, etc.) over which the aggregation isperformed may be defined by the timestamps of the data samples oftimeseries provided as an input to the DAG.

In operation, sample aggregator 308 can use the DAG to identify theduration of the time window (e.g., an hour, a day, a week, etc.) overwhich the data aggregation operation will be performed. Sampleaggregator 308 can use the timestamps of the data samples in thetimeseries provided as an input to the DAG to identify the location ofthe time window (i.e., the start time and the end time). Sampleaggregator 308 can set the start time and end time of the time windowsuch that the time window has the identified duration and includes thetimestamps of the data samples. In some embodiments, the time windowsare fixed, having predefined start times and end times (e.g., thebeginning and end of each hour, day, week, etc.). In other embodiments,the time windows may be sliding time windows, having start times and endtimes that depend on the timestamps of the data samples in the inputtimeseries.

Referring now to FIG. 10A, an example of a DAG 1000 which can be createdby DAG generator 320 is shown, according to an exemplary embodiment. DAG1000 is shown as a structured tree representing a graph of the dataflowrather than a formal scripting language. Blocks 1002 and 1004 representthe input timeseries which can be specified by timeseries ID (e.g., ID123, ID 456, etc.). Blocks 1006 and 1008 are functional blocksrepresenting data cleansing operations. Similarly, block 1010 is afunctional block representing a weekly rollup aggregation and block 1012is a functional block representing an addition operation. Blocks 1014and 1016 represent storage operations indicating where the output of DAG1000 should be stored (e.g., local storage, hosted storage, etc.).

In DAG 1000, the arrows connecting blocks 1002-1016 represent the flowof data and indicate the sequence in which the operations defined by thefunctional blocks should be performed. For example, the cleansingoperation represented by block 1006 will be the first operationperformed on the timeseries represented by block 1002. The output of thecleansing operation in block 1006 will then be provided as an input toboth the aggregation operation represented by block 1010 and theaddition operation represented by block 1012. Similarly, the cleansingoperation represented by block 1008 will be the first operationperformed on the timeseries represented by block 1004. The output of thecleansing operation in block 1008 will then be provided as an input tothe addition operation represented by block 1012.

In some embodiments, DAG 1000 can reference other DAGs as inputs.Timeseries processing engine 304 can stitch the DAGs together intolarger groups. DAG 1000 can support both scalar operators (e.g., runthis function on this sample at this timestamp) and aggregate windowoperators (e.g., apply this function over all the values in thetimeseries from this time window). The time windows can be arbitrary andare not limited to fixed aggregation windows. Logical operators can beused to express rules and implement fault detection algorithms. In someembodiments, DAG 1000 supports user-defined functions and user-definedaggregates.

In some embodiments, DAG 1000 is created based on user input. A user candrag-and-drop various input blocks 1002-1004, functional blocks1006-1012, and output blocks 1014-1016 into DAG 1000 and connect themwith arrows to define a sequence of operations. The user can edit theoperations to define various parameters of the operations. For example,the user can define parameters such as upper and lower bounds for thedata cleansing operations in blocks 1006-1008 and an aggregationinterval for the aggregation operation in block 1010. DAG 1000 can becreated and edited in a graphical drag-and-drop flow editor withoutrequiring the user to write or edit any formal code. In someembodiments, DAG generator 320 is configured to automatically generatethe formal code used by timeseries operators 306 based on DAG 1000.

Referring now to FIG. 10B, an example of code 1050 which can begenerated by DAG generator 320 is shown, according to an exemplaryembodiment. Code 1050 is shown as a collection of JSON objects 1052-1056that represent the various operations defined by DAG 1000. Each JSONobject corresponds to one of the functional blocks in DAG 1000 andspecifies the inputs/sources, the computation, and the outputs of eachblock. For example, object 1052 corresponds to the cleansing operationrepresented by block 1006 and defines the input timeseries (i.e., “123Raw”), the particular cleansing operation to be performed (i.e.,“BoundsLimitingCleanseOP”), the parameters of the cleansing operation(i.e., “upperbound” and “lowerbound”) and the outputs of the cleansingoperation (i.e., “123_Cleanse” and “BLCleanseFlag”).

Similarly, object 1054 corresponds to the aggregation operationrepresented by block 1010 and defines the input timeseries (i.e.,“123_Cleanse”), the aggregation operation to be performed (i.e.,“AggregateOP”), the parameter of the aggregation operation (i.e.,“interval”: “week”) and the output of the aggregation operation (i.e.,“123_WeeklyRollup”). Object 1056 corresponds to the addition operationrepresented by block 1012 and defines the input timeseries (i.e.,“123_Cleanse” and “456 Cleanse”), the addition operation to be performed(i.e., “AddOP”), and the output of the addition operation (i.e.,“123+456”). Although not specifically shown in FIG. 10B, code 1050 mayinclude an object for each functional block in DAG 1000.

Advantageously, the declarative views defined by the DAGs provide acomprehensive view of the operations applied to various inputtimeseries. This provides flexibility to run the workflow defined by aDAG at query time (e.g., when a request for derived timeseries data isreceived) or prior to query time (e.g., when new raw data samples arereceived, in response to a defined event or trigger, etc.). Thisflexibility allows timeseries processing engine 304 to perform some orall of their operations ahead of time and/or in response to a requestfor specific derived data timeseries.

Referring again to FIG. 3, timeseries processing engine 304 is shown toinclude a DAG optimizer 318. DAG optimizer 318 can be configured tocombine multiple DAGs or multiple steps of a DAG to improve theefficiency of the operations performed by timeseries operators 306. Forexample, suppose that a DAG has one functional block which adds“Timeseries A” and “Timeseries B” to create “Timeseries C” (i.e., A+B=C)and another functional block which adds “Timeseries C” and “TimeseriesD” to create “Timeseries E” (i.e., C+D=E). DAG optimizer 318 can combinethese two functional blocks into a single functional block whichcomputes “Timeseries E” directly from “Timeseries A,” “Timeseries B,”and “Timeseries D” (i.e., E=A+B+D). Alternatively, both “Timeseries C”and “Timeseries E” can be computed in the same functional block toreduce the number of independent operations required to process the DAG.

In some embodiments, DAG optimizer 318 combines DAGs or steps of a DAGin response to a determination that multiple DAGs or steps of a DAG willuse similar or shared inputs (e.g., one or more of the same inputtimeseries). This allows the inputs to be retrieved and loaded oncerather than performing two separate operations that both load the sameinputs. In some embodiments, DAG optimizer 318 schedules timeseriesoperators 306 to nodes where data is resident in memory in order tofurther reduce the amount of data required to be loaded from timeseriesstorage 214.

Entity Graph

Referring now to FIG. 11A, an entity graph 1100 is shown, according tosome embodiments. In some embodiments, entity graph 1100 is generated orused by data collector 212, as described with reference to FIG. 2.Entity graph 1100 describes how collection of devices and spaces areorganized and how the different devices and spaces relate to each other.For example, entity graph 1100 is shown to include an organization 1102,a space 1104, a system 1106, a point 1108, and a timeseries 1109. Thearrows interconnecting organization 1102, space 1104, system 1106, point1108, and timeseries 1109 identify the relationships between suchentities. In some embodiments, the relationships are stored asattributes of the entity described by the attribute.

Organization 1102 is shown to include a contains descendants attribute1110, a parent ancestors attribute 1112, a contains attribute 1114, alocated in attribute 1116, an occupied by ancestors attribute 1118, andan occupies by attribute 1122. The contains descendants attribute 1110identifies any descendant entities contained within organization 1102.The parent ancestors attribute 1112 identifies any parent entities toorganization 1102. The contains attribute 1114 identifies any otherorganizations contained within organization 1102. The asterisk alongsidethe contains attribute 1114 indicates that organization 1102 can containany number of other organizations. The located in attribute 1116identifies another organization within which organization 1102 islocated. The number 1 alongside the located in attribute 1116 indicatesthat organization 1102 can be located in exactly one other organization.The occupies attribute 1122 identifies any spaces occupied byorganization 1102. The asterisk alongside the occupies attribute 1122indicates that organization 1102 can occupy any number of spaces.

Space 1104 is shown to include an occupied by attribute 1120, anoccupied by ancestors attribute 1118, a contains space descendantsattribute 1124, a located in ancestors attribute 1126, a contains spacesattribute 1128, a located in attribute 1130, a served by systemsattribute 1138, and a served by system descendants attribute 1134. Theoccupied by attribute 1120 identifies an organization occupied by space1104. The number 1 alongside the occupied by attribute 1120 indicatesthat space 1104 can be occupied by exactly one organization. Theoccupied by ancestors attribute 1118 identifies one or more ancestors toorganization 1102 that are occupied by space 1104. The asteriskalongside the occupied by ancestors attribute 1118 indicates that space1104 can be occupied by any number of ancestors.

The contains space descendants attribute 1124 identifies any descendantsto space 1104 that are contained within space 1104. The located inancestors attribute 1126 identifies any ancestors to space 1104 withinwhich space 1104 is located. The contains spaces attribute 1128identifies any other spaces contained within space 1104. The asteriskalongside the contains spaces attribute 1128 indicates that space 1104can contain any number of other spaces. The located in attribute 1130identifies another space within which space 1104 is located. The number1 alongside the located in attribute 1130 indicates that space 1104 canbe located in exactly one other space. The served by systems attribute1138 identifies any systems that serve space 1104. The asteriskalongside the served by systems attribute 1138 indicates that space 1104can be served by any number of systems. The served by system descendantsattribute 1134 identifies any descendent systems that serve space 1104.The asterisk alongside the served by descendant systems attribute 1134indicates that space 1104 can be served by any number of descendantsystems.

System 1106 is shown to include a serves spaces attribute 1136, a servesspace ancestors attribute 1132, a subsystem descendants attribute 1140,a part of ancestors attribute 1142, a subsystems attribute 1144, a partof attribute 1146, and a points attribute 1150. The serves spacesattribute 1136 identifies any spaces that are served by system 1106. Theasterisk alongside the serves spaces attribute 1136 indicates thatsystem 1106 can serve any number of spaces. The serves space ancestorsattribute 1132 identifies any ancestors to space 1104 that are served bysystem 1106. The asterisk alongside the serves ancestor spaces attribute1132 indicates that system 1106 can serve any number of ancestor spaces.

The subsystem descendants attribute 1140 identifies any subsystemdescendants of other systems contained within system 1106. The part ofancestors attribute 1142 identifies any ancestors to system 1106 thatsystem 1106 is part of. The subsystems attribute 1144 identifies anysubsystems contained within system 1106. The asterisk alongside thesubsystems attribute 1144 indicates that system 1106 can contain anynumber of subsystems. The part of attribute 1146 identifies any othersystems that system 1106 is part of. The number 1 alongside the part ofattribute 1146 indicates that system 1106 can be part of exactly oneother system. The points attribute 1150 identifies any data points thatare associated with system 1106. The asterisk alongside the pointsattribute 1150 indicates that any number of data points can beassociated with system 1106.

Point 1108 is shown to include a used by system attribute 1148. Theasterisk alongside the used by system attribute 1148 indicates thatpoint 1108 can be used by any number of systems. Point 1108 is alsoshown to include a used by timeseries attribute 1154. The asteriskalongside the used by timeseries attribute 1154 indicates that point1108 can be used by any number of timeseries (e.g., raw data timeseriesvirtual point timeseries, data rollup timeseries, etc.). For example,multiple virtual point timeseries can be based on the same actual datapoint 1108. In some embodiments, the used by timeseries attribute 1154is treated as a list of timeseries that subscribe to changes in value ofdata point 1108. When the value of point 1108 changes, the timeserieslisted in the used by timeseries attribute 1154 can be identified andautomatically updated to reflect the changed value of point 1108.

Timeseries 1109 is shown to include a uses point attribute 1152. Theasterisk alongside the uses point attribute 1152 indicates thattimeseries 1109 can use any number of actual data points. For example, avirtual point timeseries can be based on multiple actual data points. Insome embodiments, the uses point attribute 1152 is treated as a list ofpoints to monitor for changes in value. When any of the pointsidentified by the uses point attribute 1152 are updated, timeseries 1109can be automatically updated to reflect the changed value of the pointsused by timeseries 1109.

Timeseries 1109 is also shown to include a used by timeseries attribute1156 and a uses timeseries attribute 1158. The asterisks alongside theused by timeseries attribute 1156 and the uses timeseries attribute 1158indicate that timeseries 1109 can be used by any number of othertimeseries and can use any number of other timeseries. For example, botha data rollup timeseries and a virtual point timeseries can be based onthe same raw data timeseries. As another example, a single virtual pointtimeseries can be based on multiple other timeseries (e.g., multiple rawdata timeseries). In some embodiments, the used by timeseries attribute1156 is treated as a list of timeseries that subscribe to updates intimeseries 1109. When timeseries 1109 is updated, the timeseries listedin the used by timeseries attribute 1156 can be identified andautomatically updated to reflect the change to timeseries 1109.Similarly, the uses timeseries attribute 1158 can be treated as a listof timeseries to monitor for updates. When any of the timeseriesidentified by the uses timeseries attribute 1158 are updated, timeseries1109 can be automatically updated to reflect the updates to the othertimeseries upon which timeseries 1109 is based.

Referring now to FIG. 11B, an example of an entity graph 1160 for aparticular system of devices is shown, according to some embodiments.Entity graph 1160 is shown to include an organization 1161 (“ACMECorp”). Organization 1161 be a collection of people, a legal entity, abusiness, an agency, or other type of organization. Organization 1161occupies space 1163 (“Milwaukee Campus”), as indicated by the occupiesattribute 1164. Space 1163 is occupied by organization 1161, asindicated by the occupied by attribute 1162.

In some embodiments, space 1163 is a top level space in a hierarchy ofspaces. For example, space 1163 can represent an entire campus (i.e., acollection of buildings). Space 1163 can contain various subspaces(e.g., individual buildings) such as space 1165 (“Building 1”) and space1173 (“Building 2”), as indicated by the contains attributes 1168 and1180. Spaces 1165 and 1180 are located in space 1163, as indicated bythe located in attribute 1166. Each of spaces 1165 and 1173 can containlower level subspaces such as individual floors, zones, or rooms withineach building. However, such subspaces are omitted from entity graph1160 for simplicity.

Space 1165 is served by system 1167 (“ElecMainMeter1”) as indicated bythe served by attribute 1172. System 1167 can be any system that servesspace 1165 (e.g., a HVAC system, a lighting system, an electricalsystem, a security system, etc.). The serves attribute 1170 indicatesthat system 1167 serves space 1165. In entity graph 1160, system 1167 isshown as an electrical system having a subsystem 1169(“LightingSubMeter1”) and a subsystem 1171 (“PlugLoadSubMeter2”) asindicated by the subsystem attributes 1176 and 1178. Subsystems 1169 and1171 are part of system 1167, as indicated by the part of attribute1174.

Space 1173 is served by system 1175 (“ElecMainMeter2”) as indicated bythe served by attribute 1184. System 1175 can be any system that servesspace 1173 (e.g., a HVAC system, a lighting system, an electricalsystem, a security system, etc.). The serves attribute 1182 indicatesthat system 1175 serves space 1173. In entity graph 1160, system 1175 isshown as an electrical system having a subsystem 1177(“LightingSubMeter3”) as indicated by the subsystem attribute 1188.Subsystem 1177 is part of system 1175, as indicated by the part ofattribute 1186.

In addition to the attributes shown in FIG. 11B, entity graph 1160 caninclude “ancestors” and “descendants” attributes on each entity in thehierarchy. The ancestors attribute can identify (e.g., in a flat list)all of the entities that are ancestors to a given entity. For example,the ancestors attribute for space 1165 may identify both space 1163 andorganization 1161 as ancestors. Similarly, the descendants attribute canidentify all (e.g., in a flat list) of the entities that are descendantsof a given entity. For example, the descendants attribute for space 1165may identify system 1167, subsystem 1169, and subsystem 1171 asdescendants. This provides each entity with a complete listing of itsancestors and descendants, regardless of how many levels are included inthe hierarchical tree. This is a form of transitive closure.

In some embodiments, the transitive closure provided by the descendantsand ancestors attributes allows entity graph 1160 to facilitate simplequeries without having to search multiple levels of the hierarchicaltree. For example, the following query can be used to find all metersunder the Milwaukee Campus space 1163:

  /Systems?$filter=(systemType eq Jci.Be.Data.SystemType′Meter′) andancestorSpaces/any(a:a/name eq ′Milwaukee Campus′)and can be answered using only the descendants attribute of theMilwaukee Campus space 1163. For example, the descendants attribute ofspace 1163 can identify all meters that are hierarchically below space1163. The descendants attribute can be organized as a flat list andstored as an attribute of space 1163. This allows the query to be servedby searching only the descendants attribute of space 1163 withoutrequiring other levels or entities of the hierarchy to be searched.

Referring now to FIG. 12, an object relationship diagram 1200 is shown,according to some embodiments. Relationship diagram 1200 is shown toinclude an entity template 1202, a point 1204, a timeseries 1206, and asample 1208. In some embodiments, entity template 1202, point 1204,timeseries 1206, and sample 1208 are stored as data objects withinmemory 210 or timeseries storage 214. Relationship diagram 1200illustrates the relationships between entity template 1202, point 1204,and timeseries 1206.

Entity template 1202 can include various attributes such as an IDattribute, a name attribute, a properties attribute, and a relationshipsattribute. The ID attribute can be provided as a text string andidentifies a unique ID for entity template 1202. The name attribute canalso be provided as a text string and identifies the name of entitytemplate 1202. The properties attribute can be provided as a vector andidentifies one or more properties of entity template 1202. Therelationships attribute can also be provided as a vector and identifiesone or more relationships of entity template 1202.

Point 1204 can include various attributes such as an ID attribute, anentity template ID attribute, a timeseries attribute, and a units IDattribute. The ID attribute can be provided as a text string andidentifies a unique ID for point 1204. The entity template ID attributecan also be provided as a text string and identifies the entity template1202 associated with point 1204 (e.g., by listing the ID attribute ofentity template 1202). Any number of points 1204 can be associated withentity template 1202. However, in some embodiments, each point 1204 isassociated with a single entity template 1202. The timeseries attributecan be provided as a text string and identifies any timeseriesassociated with point 1204 (e.g., by listing the ID string of anytimeseries 1206 associated with point 1204). The units ID attribute canalso be provided as a text string and identifies the units of thevariable quantified by point 1204.

Timeseries 1206 can include various attributes such as an ID attribute,a samples attribute, a transformation type attribute, and a units IDattribute. The ID attribute can be provided as a text string andidentifies a unique ID for timeseries 1206. The unique ID of timeseries1206 can be listed in the timeseries attribute of point 1204 toassociate timeseries 1206 with point 1204. Any number of timeseries 1206can be associated with point 1204. Each timeseries 1206 is associatedwith a single point 1204. The samples attribute can be provided as avector and identifies one or more samples associated with timeseries1206. The transformation type attribute identifies the type oftransformation used to generate timeseries 1206 (e.g., average hourly,average daily, average monthly, etc.). The units ID attribute can alsobe provided as a text string and identifies the units of the variablequantified by timeseries 1206.

Sample 1208 can include a timestamp attribute and a value attribute. Thetimestamp attribute can be provided in local time and can include anoffset relative to universal time. The value attribute can include adata value of sample 1208. In some instances, the value attribute is anumerical value (e.g., for measured variables). In other instances, thevalue attribute can be a text string such as “Fault” if sample 1208 ispart of a fault detection timeseries.

Nested Stream Generation

Referring now to FIGS. 13A-15B, web services platform 102 can beconfigured to generate nested streams of timeseries data. Nested streamscan include various types of derived timeseries created by processingDAGs. For example, nested streams can include data rollup timeseries,virtual point timeseries, weather point timeseries, fault detectiontimeseries, assigned state timeseries, abnormal event timeseries, and/orany other type of derived timeseries previously described. In someembodiments, the nested streams are created from input timeseriesretrieved from timeseries storage 214 (as described with reference toFIGS. 13A-13B). In other embodiments, the nested streams are createdfrom streaming data received in real-time from IoT devices 203 and/orother data sources (as described with reference to FIG. 14). In someembodiments, the nested streams are used as an intermediate timeseriesin a timeseries processing workflow. For example, a first derivedtimeseries can be created by processing a first DAG and used as an inputto a second DAG to create a second derived timeseries (as described withreference to FIGS. 15A-15B).

Timeseries Processing Workflow

Referring particularly to FIG. 13A, a block diagram illustrating atimeseries processing workflow 1300 is shown, according to an exemplaryembodiment. Workflow 1300 may be performed by workflow manager 322 incombination with other components of timeseries service 228. Workflow1300 is shown to include performing a read of the timeseries data (step1302). Step 1302 may include reading raw data samples and/or the deriveddata samples provided by timeseries storage interface 316. Thetimeseries data may be stored in timeseries storage 214. In someembodiments, timeseries storage 214 includes on-site data storage (e.g.,Redis, PostgreSQL, etc.) and/or cloud data storage (e.g., Azure Redis,DocDB, HBase, etc.).

Timeseries storage interface 316 can be configured to read and write atimeseries collection, a samples collection, and a post sample request(PSR) collection. Each of these collections can be stored in timeseriesstorage 214. The timeseries collection may contain all the timeseriesregistered in workflow manager 322. The timeseries collection may alsocontain the DAG for each timeseries. The timeseries collection can beused by workflow manager 322 to accept only PSRs related to validtimeseries registered in workflow manager 322. The timeseries collectioncan also be used in steps 1314-1316 to lookup the DAG for a specifictimeseries ID.

In some embodiments, the entire timeseries collection is loaded intolocal memory. The timeseries collection can be a regular collection or apartitioned collection (e.g., one partition for approximately every 100timeseries). In some embodiments, the timeseries collection containsabout 200,000 to 250,000 timeseries. The ID for each document in thetimeseries collection may be the timeseries ID. The DAG for eachtimeseries may contain a set of operations and/or transformations thatneed to be performed to generate the derived timeseries data based onthe timeseries. On registration of a new timeseries, the DAG for thetimeseries can be selected from DAG templates. The DAG template mayinclude a set of standard operations applicable to the timeseries. Ondefinition of a new metric for a timeseries, the new metric and the listof operations to generate that metric can be added to the DAG.

The samples collection may contain all of the timeseries samples (e.g.,raw samples, derived timeseries samples). The samples collection can beused for all GET requests for a specific timeseries ID. A portion of thesamples collection can be stored in local memory (e.g., past 48 hours)whereas the remainder of the samples collection can be stored intimeseries storage 214. The samples collection may act as a partitionedcollection instead of a regular collection to improve efficiency andperformance. In some embodiments, the samples collection is stored in aJSON format and partitioned on timeseries ID. The ID field may be uniquefor each partition and may have the form “Metric: Timestamp.”

The PSR collection may contain all of the PSRs and can be used toprovide status updates to the user for a PSR related to a specifictimeseries ID. A portion of the PSR collection can be stored in localmemory (e.g., past 48 hours) whereas the remainder of the PSR collectioncan be stored in timeseries storage 214. The PSR collection can bepartitioned on timeseries ID. In some embodiments, the ID for eachdocument in the PSR collection has the form “TimeseriesID: Timestamp.”

Still referring to FIG. 13A, workflow 1300 is shown to include acceptinga PSR (step 1304). Step 1304 may be performed by executing a PSRprocess. In some embodiments, the PSR process receives a PSR anddetermines whether the PSR contains more than one timeseries ID. Inresponse to a determination that the PSR contains more than onetimeseries ID, the PSR process may break the PSR into multiple PSRs,each of which is limited to a single timeseries ID. The PSRs can beprovided to PSR event hub 1306. PSR event hub 1306 can be configured tostore PSR events. Each PSR event may include a PSR for one timeseriesID. In some embodiments, each PSR event is stored in the form“TimeseriesID:Timestamp.”

Workflow 1300 is shown to include deduplicating raw samples (step 1308).Step 1308 may be performed by executing a deduplication process. In someembodiments, the deduplication process includes accepting PSR eventsfrom PSR event hub 1306 and splitting each PSR into a list of samples.Step 1308 may include tagging each sample as a new sample, an updatedsample, or a duplicate sample. New samples and updated samples can besent to raw samples event hub 1310, whereas duplicate samples may bediscarded. In some embodiments, step 1308 is deployed on Azure usingAzure Worker Roles. Step 1308 can include checking for duplicate samplesin timeseries storage 214 as well as the samples that are currently inraw samples event hub 1310.

In some embodiments, the deduplication process in step 1308 removes allduplicate data samples such that only a single unique copy of each datasample remains. Removing all duplicate samples may ensure that aggregateoperations produce accurate aggregate values. In other embodiments, thededuplication process in step 1308 is configured to remove most, but notall, duplicate samples. For example, the deduplication process can beimplemented using a Bloom filter, which allows for the possibility offalse positives but not false negatives. In step 1308, a false positivecan be defined as a non-duplicate new or updated sample. Accordingly,some duplicates may be flagged as non-duplicate, which introduces thepossibility that some duplicate samples may not be properly identifiedand removed. The deduplicated raw samples can be sent to raw samplesevent hub 1310.

Workflow 1300 is shown to include storing the raw samples (step 1312).Step 1312 can include accepting the raw samples from raw samples eventhub 1310 and pushing the raw samples to persistent storage. In someembodiments, step 1312 is deployed on Azure using Azure Worker Roles.The worker role may generate requests at a rate based on X % of thecapacity of the storage. For example, if the capacity of the storage is10,000 storage units and X % is 20% (e.g., 20% of the storage throughputis reserved for sample writes), and each write takes 5 storage units,step 1312 may generate a total of 400 writes per second

$\left( {{i.e.},{\frac{10,000*20\%}{5} = 400}} \right).$

Workflow 1300 is shown to include generating an event trigger DAG (step1314). Step 1314 can be performed by executing an event trigger DAGprocess. Step 1314 may include accepting events (samples) from rawsamples event hub 1310. For each sample event, step 1314 may includeidentifying the timeseries ID of the sample and accessing the timeseriescollection to obtain the DAG for the corresponding timeseries. Step 1314may include identifying each derived data timeseries generated by theDAG and each operation included in the DAG. In some embodiments, step1314 tags each operation to indicate whether the operation should besent to the C# engine 1332 or the Python engine 1334 for execution. Step1314 may include identifying and fetching any additional data (e.g.,samples, timeseries, parameters, etc.) which may be necessary to performthe operations defined by the DAG. Step 1314 may generate an enrichedDAG which includes the original DAG along with all the data necessary toperform the operations defined by the DAG. The enriched DAG can be sentto the DAG event hub 1318.

In some embodiments, workflow 1300 includes generating a clock triggerDAG (step 1316). Step 1316 can be performed by executing a clock triggerDAG process. Step 1316 may be similar to step 1314. However, step 1316may be performed in response to a clock trigger rather than in responseto receiving a raw sample event. The clock trigger can periodicallytrigger step 1316 to perform batch queries (e.g., every hour). Step 1316may include identifying a timeseries ID specified in the clock triggerand accessing the timeseries collection to obtain the DAG for thecorresponding timeseries. Step 1316 may include identifying each deriveddata timeseries generated by the DAG and each operation included in theDAG. In some embodiments, step 1316 tags each operation to indicatewhether the operation should be sent to the C# engine 1332 or the Pythonengine 1334 for execution. Step 1316 may include identifying andfetching any additional data (e.g., samples, timeseries, parameters,etc.) which may be necessary to perform the operations defined by theDAG. Step 1316 may generate an enriched DAG which includes the originalDAG along with all the data necessary to perform the operations definedby the DAG. The enriched DAG can be sent to the DAG event hub 1318.

DAG event hub 1318 can be configured to store enriched DAG events. Eachenriched DAG event can include an enriched DAG. The enriched DAG mayinclude a DAG for a particular timeseries along with all the datanecessary to perform the operations defined by the DAG. DAG event hub1318 can provide the enriched DAG events to step 1320.

Still referring to FIG. 13A, workflow 1300 is shown to include runningthe DAG (step 1320). Step 1320 can include accepting enriched DAG eventsfrom DAG event hub 1318 and running through the sequence of operationsdefined by the DAG. Workflow manager 322 can submit each operation inseries to execution engines 1330 and wait for results before submittingthe next operation. Execution engines 1330 may include a C# engine 1332,a Python engine 1334, or any other engine configured to perform theoperations defined by the DAG. In some embodiments, execution engines1330 include timeseries operators 306. When a given operation iscomplete, execution engines 1330 can provide the results of theoperation to workflow manager 322. Workflow manager 322 can use theresults of one or more operations as inputs for the next operation,along with any other inputs that are required to perform the operation.In some embodiments, the results of the operations are the derivedtimeseries samples. The derived timeseries samples can be provided toderived timeseries event hub 1322.

Derived timeseries event hub 1322 can be configured to store derivedtimeseries sample. Each derived timeseries sample may include a sampleof a derived timeseries. The derived timeseries may include the resultsof the operations performed by execution engines 1330. Derivedtimeseries event hub 1322 can provide the derived timeseries samples tostep 1324.

Workflow 1300 is shown to include storing the derived timeseries samples(step 1324). Step 1324 can include accepting derived timeseries samplesfrom derived timeseries event hub 1322 and storing the derivedtimeseries samples in persistent storage (e.g., timeseries storage 214).In some embodiments, step 1324 is deployed on Azure using Azure WorkerRoles. The worker role may generate requests at a rate based on Y % ofthe capacity of the storage. For example, if the capacity of the storageis 10,000 storage units and Y % is 50% (e.g., 50% of the storagethroughput is reserved for sample writes), and each write takes 5storage units, step 1324 may generate a total of 1,000 writes per second

$\left( {{i.e.},{\frac{10,000*50\%}{5} = {1,000}}} \right).$

Referring now to FIG. 13B, a flowchart of a process 1350 for obtainingand processing timeseries data is shown, according to an exemplaryembodiment. Process 1350 can be performed by workflow manager 322 incombination with other components of timeseries service 228. Process1350 is shown to include obtaining samples of a timeseries fromtimeseries storage (step 1352). Step 1352 may include obtaining raw datasamples and/or derived data samples via timeseries storage interface316. The samples of the timeseries may be obtained from timeseriesstorage 214 or received in real-time from a sensor or other device thatgenerates the samples. Step 1352 can include loading the entiretimeseries or a subset of the samples of the timeseries into localmemory. For example, some of the samples of the timeseries may be storedin local memory (e.g., past 48 hours) whereas the remainder of thesamples of the timeseries can be stored in timeseries storage 214.

Process 1350 is shown to include handling a post-sample request (PSR)associated with the timeseries (step 1354). The PSR may be obtained froma PSR collection via timeseries storage interface 316. The PSR can beused to provide status updates to the user for a specific timeseries ID.In some embodiments, step 1354 includes receiving a PSR and determiningwhether the PSR contains more than one timeseries ID. In response to adetermination that the PSR contains more than one timeseries ID, step1354 may include breaking the PSR into multiple PSRs, each of which islimited to a single timeseries ID. The PSRs can be provided to PSR eventhub 1306 and stored as PSR events. Each PSR event may include a PSR forone timeseries ID. In some embodiments, each PSR event is stored in theform “TimeseriesID: Timestamp.”

Process 1350 is shown to include deduplicating samples of the timeseries(step 1356). Step 1356 may be performed by executing a deduplicationprocess. In some embodiments, the deduplication process includesaccepting PSR events from PSR event hub 1306 and splitting each PSR intoa list of samples. Step 1356 may include tagging each sample as a newsample, an updated sample, or a duplicate sample. New samples andupdated samples can be sent to raw samples event hub 1310, whereasduplicate samples may be discarded. In some embodiments, step 1356 isdeployed on Azure using Azure Worker Roles. Step 1356 can includechecking for duplicate samples in timeseries storage 214 as well as thesamples that are currently in raw samples event hub 1310.

In some embodiments, the deduplication process in step 1356 removes allduplicate data samples such that only a single unique copy of each datasample remains. Removing all duplicate samples may ensure that aggregateoperations produce accurate aggregate values. In other embodiments, thededuplication process in step 1356 is configured to remove most, but notall, duplicate samples. For example, the deduplication process can beimplemented using a Bloom filter, which allows for the possibility offalse positives but not false negatives. In step 1356, a false positivecan be defined as a non-duplicate new or updated sample. Accordingly,some duplicates may be flagged as non-duplicate, which introduces thepossibility that some duplicate samples may not be properly identifiedand removed. The deduplicated samples can be sent to raw samples eventhub 1310.

Still referring to FIG. 13B, process 1350 is shown to includeidentifying one or more stored DAGs that use the timeseries as an input(step 1358). Step 1358 can include obtaining the stored DAGs viatimeseries via timeseries storage interface 316 and identifying therequired timeseries inputs of each DAG. For each DAG that uses thetimeseries as an input, process 1350 can identify the timeseriesprocessing operations defined by the DAG (step 1360). The timeseriesprocessing operations can include data cleansing operations, dataaggregation operations, timeseries adding operations, virtual pointcalculation operations, or any other type of operation that can beapplied to one or more input timeseries.

Process 1350 is shown to include identifying and obtaining samples ofany timeseries required to perform the timeseries processing operations(step 1362). The timeseries can be identified by inspecting the inputsrequired by each of the timeseries processing operations identified instep 1360. For example, DAG 1000 in FIG. 10A is shown to include both“Timeseries ID: 123” and “Timeseries ID: 456” as required inputs.Assuming that samples of the timeseries ID 123 are obtained in step1352, DAG 1000 can be identified in step 1358 as a DAG that uses thetimeseries ID 123 as an input. The timeseries identified in step 1362can include timeseries ID 123, timeseries ID 456, or any othertimeseries used as an input to DAG 1000. Step 1362 may includeidentifying and fetching any additional data (e.g., samples, timeseries,parameters, etc.) which may be necessary to perform the operationsdefined by the DAG.

In some embodiments, the samples obtained in step 1362 are based on thetimeseries processing operations defined by the DAG, as well as thetimestamps of the original samples obtained in step 1352. For example,the DAG may include a data aggregation operation that aggregates aplurality of data samples having timestamps within a given time window.The start time and end time of the time window may be defined by the DAGand the timeseries to which the DAG is applied. The DAG may define theduration of the time window over which the data aggregation operationwill be performed. For example, the DAG may define the aggregationoperation as an hourly aggregation (i.e., to produce an hourly datarollup timeseries), a daily aggregation (i.e., to produce a daily datarollup timeseries), a weekly aggregation (i.e., to produce a weekly datarollup timeseries), or any other aggregation duration. The position ofthe time window (e.g., a specific day, a specific week, etc.) over whichthe aggregation is performed may be defined by the timestamps of thesamples obtained in step 1352.

Step 1362 can include using the DAG to identify the duration of the timewindow (e.g., an hour, a day, a week, etc.) over which the dataaggregation operation will be performed. Step 1362 can include using thetimestamps of the data samples obtained in step 1352 identify thelocation of the time window (i.e., the start time and the end time).Step 1362 can include setting the start time and end time of the timewindow such that the time window has the identified duration andincludes the timestamps of the data samples obtained in step 1352. Insome embodiments, the time windows are fixed, having predefined starttimes and end times (e.g., the beginning and end of each hour, day,week, etc.). In other embodiments, the time windows may be sliding timewindows, having start times and end times that depend on the timestampsof the data samples in the input timeseries. Once the appropriate timewindow has been set and the other input timeseries are identified, step1362 can obtain samples of any input timeseries to the DAG that havetimestamps within the appropriate time window. The input timeseries caninclude the original timeseries identified in step 1352 and any othertimeseries used as input to the DAG.

Process 1350 is shown to include generating an enriched DAG includingthe original DAG and all timeseries samples required to perform thetimeseries processing operations (step 1364). The original DAG may bethe DAG identified in step 1358. The timeseries samples required toperform the timeseries processing operations may include any of thetimeseries samples obtained in step 1362. In some embodiments, step 1364includes identifying each derived data timeseries generated by the DAGand each operation included in the DAG. In some embodiments, step 1364tags each operation to indicate a particular execution engine (e.g., C#engine 1332, Python engine 1334, etc.) to which the processing operationshould be sent for execution.

Process 1350 is shown to include executing the enriched DAG to generateone or more derived timeseries (step 1366). Step 1366 can includesubmitting each timeseries processing operation in series to executionengines 1330 and waiting for results before submitting the nextoperation. When a given operation is complete, execution engines 1330can provide the results of the operation to workflow manager 322.Process 1350 can use the results of one or more operations as inputs forthe next operation, along with any other inputs that are required toperform the operation. In some embodiments, the results of theoperations are the derived timeseries samples.

Process 1350 is shown to include storing the derived timeseries in thetimeseries storage (step 1368). The derived timeseries may include theresults of the operations performed in step 1366. Step 1368 can includeaccepting derived timeseries samples from derived timeseries event hub1322 and storing the derived timeseries samples in persistent storage(e.g., timeseries storage 214). In some embodiments, step 1368 isdeployed on Azure using Azure Worker Roles. The worker role may generaterequests at a rate based on Y % of the capacity of the storage. Forexample, if the capacity of the storage is 10,000 storage units and Y %is 50% (e.g., 50% of the storage throughput is reserved for samplewrites), and each write takes 5 storage units, step 1368 may generate atotal of 1,000 writes per second

$\left( {{i.e.},{\frac{10,000*50\%}{5} = {1,000}}} \right).$

Streaming Data Processing

Referring now to FIG. 14, a system 1400 for processing streaming data isshown, according to an exemplary embodiment. System 1400 can beimplemented as part of web services platform 102 and may include varioussystems or devices configured to collect and process timeseries data.For example, system 1400 is shown to include IoT devices 203, timeseriesservice 228, a weather service 152, timeseries storage 214.

IoT devices 203 may include any of a variety of sensors 1404, physicaldevices or equipment 1406 (e.g., actuators, electronics, vehicles, homeappliances, etc.) and/or other items having network connectivity whichenable IoT devices 203 to communicate with web services platform 102.For example, IoT devices 203 can include smart home hub devices, smarthouse devices, doorbell cameras, air quality sensors, smart switches,smart lights, smart appliances, garage door openers, smoke detectors,heart monitoring implants, biochip transponders, cameras streaming livefeeds, automobiles with built-in sensors, DNA analysis devices, fieldoperation devices, tracking devices for people/vehicles/equipment,networked sensors, wireless sensors, wearable sensors, environmentalsensors, RFID gateways and readers, IoT gateway devices, robots andother robotic devices, GPS devices, smart watches, virtual/augmentedreality devices, and/or other networked or networkable devices. In someembodiments, IoT devices 203 include some or all of devices 112-116,122-126, 132-136, and 142-146, as described with reference to FIG. 1.

IoT devices 203 are shown providing timeseries samples and event data totimeseries service 228. Timeseries samples can include measurementsobtained by sensors 1404. For example, sensors 1404 can collect varioustypes of measurements and send the measurements to timeseries service228. In some embodiments, each measurement includes a measured valueindicating a value of the measured variable and a timestamp indicating atime at which the variable was measured. Timeseries samples may alsoinclude monitored variables or states of equipment 1406. For example,IoT devices 203 may store internal variables that represent equipmentstates (e.g., equipment on/off, door open/closed, equipment operating at50% capacity, etc.). Each timeseries sample may include a values of aparticular variable or state and a timestamp indicating a time at whichthe variable or state was observed.

Event data may include any type of data describing various eventsobserved by IoT devices 203. Each sample of event data may include adescription or indication of the event and a timestamp indicating whenthe event occurred. For example, event data may include badge accessevents that occur when a person scans an ID badge at a card reader ofequipment 1406 (e.g., a particular badge was scanned at a particularcard reader at a particular time). Event data may include securityevents generated by security equipment (e.g., intruder detected at southentrance). Event data may include alarms or faults detected by equipment1406 and/or other types of IoT devices 203 (e.g., a particular faultoccurred within a particular device of equipment 1406 at a particulartime).

Weather service 152 is shown providing weather data to timeseriesservice 228. Weather data may include samples of various weather-relatedvariables observed by weather service 152. For example, weather data caninclude temperature data, humidity data, precipitation data, wind speeddata, cloud position data, atmospheric pressure data, and/or other typesof weather-related variables. Weather data can include current values ofthe weather-related variables, past values of the weather-relatedvariables (e.g., historical values), and/or future values of theweather-related variables (e.g., predicted or estimated values). Eachsample of the weather data may include a value of a particularweather-related variable and a timestamp indicating a time at which thecorresponding value was observed or a time for which the correspondingvalue is predicted.

Timeseries service 228 is shown to include a timeseries identifier 1414,a DAG identifier 1416, execution engines 1330, and a timeseriesgenerator 1418. Timeseries identifier 1414 can receive the timeseriessamples and event data from IoT devices 203 and the weather data fromweather service 152. Timeseries identifier 1414 can identify atimeseries associated with each incoming data sample. The identifiedtimeseries for a particular data sample may be a raw data timeseriesstored in timeseries storage 214 that contains a series of values forthe same variable or data source. For example, timeseries can be storedin the following format:

-   -   [<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key,        timestamp₃, value₃>]        where key is an identifier of the source of the data samples        (e.g., timeseries ID, sensor ID, etc.), timestamp_(i) identifies        a time associated with the ith sample, and value_(i) indicates        the value of the ith sample. Timeseries identifier 1414 can use        attributes of the incoming data samples (e.g., data source,        sensor ID, variable ID, etc.) to identify a particular        timeseries associated with each sample and can provide the        identified timeseries ID to DAG identifier 1416.

DAG identifier 1416 can use the timeseries ID received from timeseriesidentifier 1414 to identify one or more DAGs that use the identifiedtimeseries as an input. As described above, a DAG may be a predefinedsequence of processing operations that transform one or more inputtimeseries into one or more output timeseries. Accordingly, each DAG mayhave one or more input timeseries associated therewith. In someembodiments, the input timeseries for each DAG are stored as attributesof the DAG in DAG storage 330. DAG identifier 1416 can read suchinformation from DAG storage 330 to determine which of the stored DAGsuse the identified timeseries as an input. DAG identifier 1416 can thenprovide an indication of the identified DAGs to execution engines 1330in the form of one or more DAG IDs.

As described above, execution engines 1330 can include a C# engine 1332,a Python engine 1334, or any other engine configured to perform theoperations defined by a DAG. In some embodiments, execution engines 1330include timeseries operators 306. Execution engines 1330 can receive theincoming data from IoT devices 203 and weather service 152 (i.e., thetimeseries samples, event data, and weather data), as well as the DAGIDs from DAG identifier 1416. Execution engines 1330 can execute theDAGs, using the incoming data as an input, to generate derivedtimeseries samples. Each derived timeseries sample may be the result ofa timeseries processing operation that uses an incoming data sample asan input. In some embodiments, each derived timeseries sample includes akey (e.g., a timeseries ID), a timestamp, and a value.

One type of derived timeseries sample is a virtual point sample.Execution engines 1330 can calculate virtual data points by applying anyof a variety of mathematical operations or functions to actual datapoints or other virtual data points. For example, execution engines 1330can calculate a virtual data point (pointID₃) by adding two or moreactual data points (pointID₁and pointID₂) (e.g.,pointID₃=pointID₁+pointID₂). As another example, execution engines 1330can calculate an enthalpy data point (pointID₄) based on a measuredtemperature data point (pointID₅) and a measured pressure data point(pointID₆) (e.g., pointID₄=enthalpy(pointID₅, pointID₆)). In someinstances, a virtual data point can be derived from a single actual datapoint. For example, execution engines 1330 can calculate a saturationtemperature (pointID₇) of a known refrigerant based on a measuredrefrigerant pressure (pointID₈) (e.g., pointID₇=T_(sat)(pointID₈)).

Another type of derived timeseries sample is a virtual weather pointsample. Execution engines 1330 can calculate values of virtual weatherpoint samples by applying the timeseries processing operations definedby a DAG to the incoming weather data. For example, execution engines1330 can perform weather-based calculations using the incoming weatherdata to generate values for weather-related variables such as coolingdegree days (CDD), heating degree days (HDD), cooling energy days (CED),heating energy days (HED), and normalized energy consumption. These andother examples of weather-related derived timeseries samples aredescribed in detail with reference to weather point calculator 312.

Another type of derived timeseries sample is a sample of a faultdetection timeseries. Execution engines 1330 can evaluate faultdetection rules defined by a DAG to detect faults in the incoming data.For example, execution engines 1330 can apply the fault detection rulesto the input timeseries samples to determine whether each sample of theinput timeseries qualifies as a fault. In some embodiments, each derivedtimeseries sample includes a timestamp and a fault detection value. Thetimestamp can be the same as the timestamp of the corresponding datasample of the input timeseries. The fault detection value can indicatewhether the corresponding data sample of the data timeseries qualifiesas a fault. For example, the fault detection value can have a value of“Fault” if a fault is detected and a value of “Not in Fault” if a faultis not detected in the corresponding data sample of the data timeseries.

Another type of derived timeseries sample is a sample of an abnormalevent timeseries. Execution engines 1330 can evaluate abnormal eventdetection rules defined by a DAG to detect abnormal events in the eventdata. For example, execution engines 1330 can apply the abnormal eventdetection rules to the event data to determine whether each sample ofthe event data qualifies as an abnormal event. In some embodiments, eachderived timeseries sample of an abnormal event timeseries includes atimestamp and an abnormal event value. The timestamp can be the same asthe timestamp of the corresponding sample of the event data. Theabnormal event value can indicate whether the corresponding sample ofthe event data is normal or abnormal. For example, the abnormal eventvalue can have a value of “Abnormal” if the event meets the criteria forabnormal events and a value of “Normal” if the event does not meet thecriteria for abnormal events.

In some embodiments, an event is considered abnormal if it deviatessignificantly from other similar events (e.g., events associated withthe same individual, the same space, the same equipment, etc.). Forexample, if the event data indicates that a particular person typicallybadges into a building between 8:30 AM and 9:00 AM every day, an eventindicating that the person is badging into the building at 3:00 AM maybe considered abnormal. Similarly, if the weather data indicates that aparticular building typically experiences outdoor air temperaturesbetween 30° F. and 40° F. during a particular month, a temperature of60° F. during that month may be considered abnormal.

Timeseries generator 1418 can use the derived timeseries samples togenerate various derived timeseries. The derived timeseries can includedata rollup timeseries, virtual point timeseries, weather pointtimeseries, fault detection timeseries, assigned state timeseries,abnormal event timeseries, and/or any other type of derived timeseriescreated by executing the identified DAGs. Timeseries generator 1418 canstore the derived timeseries in timeseries storage 214 or otherpersistent storage.

Iterative Timeseries Processing

Referring now to FIG. 15A, system 1400 can be configured performiterative timeseries processing. For example, timeseries identifier 1414is shown receiving an input timeseries. The input timeseries can be araw data timeseries, a derived timeseries, or a collection of timeseriessamples received from IoT devices 203 or weather service 152 inreal-time (e.g., incoming streaming data). Timeseries identifier 1414identify a timeseries ID associated with the input timeseries and canprovide the timeseries ID to DAG identifier 1416.

DAG identifier 1416 can use the timeseries ID received from timeseriesidentifier 1414 to identify one or more DAGs that use the inputtimeseries as an input. As described above, a DAG may be a predefinedsequence of processing operations that transform one or more inputtimeseries into one or more output timeseries. Accordingly, each DAG mayhave one or more input timeseries associated therewith. In someembodiments, the input timeseries for each DAG are stored as attributesof the DAG in DAG storage 330. DAG identifier 1416 can read suchinformation from DAG storage 330 to determine which of the stored DAGsuse the input timeseries as an input. DAG identifier 1416 can thenprovide an indication of the identified DAGs to execution engines 1330in the form of one or more DAG IDs.

Execution engines 1330 can execute the identified DAGs, using the inputtimeseries as an input, to generate derived timeseries samples.Timeseries generator 1418 can then assemble the derived timeseriessamples into a first derived timeseries. The first derived timeseriescan be stored in timeseries storage 214. The first derived timeseriescan also be provided as an input to timeseries identifier 1414.

Timeseries identifier 1414 can treat the first derived timeseries as aninput and the entire process can be repeated. For example, timeseriesidentifier 1414 identify a timeseries ID associated with the firstderived timeseries and can provide the timeseries ID to DAG identifier1416. DAG identifier 1416 can use the timeseries ID received fromtimeseries identifier 1414 to identify one or more DAGs that use thefirst derived timeseries as an input. DAG identifier 1416 can thenprovide an indication of the identified DAGs to execution engines 1330in the form of one or more DAG IDs. Execution engines 1330 can executethe identified DAGs, using the first derived timeseries as an input, togenerate derived timeseries samples. Timeseries generator 1418 can thenassemble the derived timeseries samples into a second derivedtimeseries. The second derived timeseries can be stored in timeseriesstorage 214.

Referring now to FIG. 15B, a flowchart of an iterative timeseriesprocessing process 1500 is shown, according to an exemplary embodiment.Process 1500 can be performed by one or more components of web servicesplatform 102 or system 1400 as previously described. Process 1500 isshown to include obtaining an input timeseries (step 1502) andidentifying a first DAG that uses the input timeseries as an input (step1504). Process 1500 is shown to include performing timeseries processingoperations defined by the DAG to generate a derived timeseries (step1506). The derived timeseries can be stored in timeseries storage 214.

The derived timeseries can then be treated as an input to timeseriesservice 228. For example, process 1500 is shown to include identifyinganother DAG that uses the derived timeseries as an input (step 1508) andperforming timeseries processing operations defined by the other DAG togenerate another derived timeseries (step 1510). In some embodiments,the DAG that uses the derived timeseries as an input is different fromthe first DAG that uses the input timeseries as an input. The derivedtimeseries created in step 1510 can be stored in timeseries storage 214.

The derived timeseries created in step 1510 can also be treated asanother input to timeseries service 228. Steps 1508-1510 can be repeatediteratively until the timeseries created in the most recent iteration ofstep 1510 is not used as an input to any of the DAGs. Each iteration ofsteps 1508-1510 may generate another derived timeseries which can bestored in timeseries storage 214.

Cloud-Based Feedback Control

Referring now to FIG. 16, a block diagram of a cloud-based feedbackcontrol system 1600 is shown, according to an exemplary embodiment.Conventional feedback control systems typically include an on-sitefeedback controller located nearby the controlled system or device. Forexample, control systems for building equipment typically include acontroller located within in the same building or facility as thebuilding equipment. The building equipment provide measurements or otherfeedback to the controller via a wired or wireless communications link(e.g., Ethernet, Wi-Fi, etc.) or local area network (LAN) within thebuilding. The controller uses the feedback from the building equipmentto generate an appropriate control signal, which is provided as acontrol input to the building equipment via the wired or wirelesscommunications link or LAN.

Cloud-based feedback control system 1600 makes use of web servicesplatform 102 to provide feedback control as a cloud-based platformservice. For example, control system 1600 is shown to include webservices platform 102, network 104, and campus 1602. In brief overview,campus 1602 provides feedback samples (e.g., measurements, samples ofmonitored variables, system states, values of points, etc.) to webservices platform 102 via network 104. Web services platform 102 usesthe feedback samples as an input to a cloud-based feedback controlalgorithm (e.g., PID, MPC, etc.) that uses the feedback samples togenerate control signal samples. In some embodiments, web servicesplatform 102 treats the feedback samples as samples of an inputtimeseries and processes the input timeseries using a feedback controlDAG. The feedback control DAG converts the feedback samples into controlsignal samples, which are a type of derived timeseries samples. Thecontrol signal samples are then provided as a control signal back tocampus 102 via network 104.

As described above, web services platform 102 can be distributed acrossmultiple processing devices and can therefore make use of multipleprocessing devices to generate the control signals. The use of multipleprocessing devices provides several advantages relative to conventionalon-site controllers that use only a single processing device. Forexample, the use of multiple remote processing devices reduces need forin-building processing devices or other on-site resources such asphysical controllers located at the building site. The use of multipleremote processing devices also reduces processing latency relative to asingle processing device by enabling parallel processing across multipledevices. For example, some portions of the feedback control DAG can beprocessed by a first processing device, whereas other portions of thefeedback control DAG can be processed by other processing devices. Thisallows for faster execution and more responsive feedback controlrelative to conventional on-site controllers with a single processingdevice.

Campus 1602 is shown to include a building management system (BMS) 1604,a central plant 1606, and IoT devices 203. A BMS is, in general, asystem of devices configured to control, monitor, and manage equipmentin or around a building or building area. For example, BMS 1604 mayinclude a HVAC system, a security system, a lighting system, a firealerting system, any other system that is capable of managing buildingfunctions or devices, or any combination thereof. An example of a BMSwhich can be used as BMS 1604 is described in detail in U.S. patentapplication Ser. No. 14/717,593, titled “Building Management System forForecasting Time Series Values of Building Variables” and filed May 20,2015, the entire disclosure of which is incorporated by referenceherein.

BMS 1604 may include a variety of building subsystems including, forexample, a building electrical subsystem, an information communicationtechnology (ICT) subsystem, a security subsystem, a HVAC subsystem, alighting subsystem, a lift/escalators subsystem, and a fire safetysubsystem. In various embodiments, the building subsystems can includefewer, additional, or alternative subsystems. For example, the buildingsubsystems can also or alternatively include a refrigeration subsystem,an advertising or signage subsystem, a cooking subsystem, a vendingsubsystem, a printer or copy service subsystem, or any other type ofbuilding subsystem that uses controllable equipment and/or sensors tomonitor or control a variable state or condition of a building.

Each of the building subsystems can include any number of devices,controllers, and connections (referred to collectively as buildingequipment 1608) for completing its individual functions and controlactivities. Building equipment 1608 can include HVAC equipment such aschillers, boilers, air handling units, economizers, field controllers,supervisory controllers, actuators, sensors (e.g., temperature,humidity, flow rate, etc.), and other devices for controlling thetemperature, humidity, airflow, or other variable conditions within abuilding. In some embodiments, building equipment 1608 includes lightingequipment such as light fixtures, ballasts, lighting sensors, dimmers,or other devices configured to controllably adjust the amount of lightprovided to a building space. In some embodiments, building equipment1608 includes security equipment such as occupancy sensors, videosurveillance cameras, digital video recorders, video processing servers,intrusion detection devices, access control devices and servers, orother security-related devices.

Central plant 1606 may include one or more subplants that consumeresources from utilities (e.g., water, natural gas, electricity, etc.)to satisfy the loads of campus 1602. For example, central plant 1606 mayinclude a heater subplant, a heat recovery chiller subplant, a chillersubplant, a cooling tower subplant, a hot thermal energy storage (TES)subplant, and a cold thermal energy storage (TES) subplant, a steamsubplant, and/or any other type of subplant configured to serve campus1602. Each of the subplants may include a variety of central plantequipment 1610 (e.g., boilers, chillers, heat recovery chillers, coolingtowers, thermal energy storage tanks, batteries, etc.). The subplantsmay be configured to convert input resources (e.g., electricity, water,natural gas, etc.) into output resources (e.g., cold water, hot water,chilled air, heated air, etc.) that are provided to buildings of campus1602. An exemplary central plant which may be used as central plant 1606is described U.S. patent application Ser. No. 14/634,609, titled “HighLevel Central Plant Optimization” and filed Feb. 27, 2015, the entiredisclosure of which is incorporated by reference herein.

IoT devices 203 may include any of a variety of sensors, physicaldevices or equipment (e.g., actuators, electronics, vehicles, homeappliances, etc.), and/or other items having network connectivity whichenable IoT devices 203 to communicate with web services platform 102.For example, IoT devices 203 can include smart home hub devices, smarthouse devices, doorbell cameras, air quality sensors, smart switches,smart lights, smart appliances, garage door openers, smoke detectors,heart monitoring implants, biochip transponders, cameras streaming livefeeds, automobiles with built-in sensors, DNA analysis devices, fieldoperation devices, tracking devices for people/vehicles/equipment,networked sensors, wireless sensors, wearable sensors, environmentalsensors, RFID gateways and readers, IoT gateway devices, robots andother robotic devices, GPS devices, smart watches, virtual/augmentedreality devices, and/or other networked or networkable devices. In someembodiments, IoT devices 203 include some or all of devices 112-116,122-126, 132-136, and 142-146, as described with reference to FIG. 1.

Campus 1602 is shown providing feedback samples to web services platform102, specifically to timeseries service 228. The feedback samples caninclude measurements obtained by sensors of building equipment 1608,central plant equipment 1610, and/or IoT devices 203. For example, thesensors can collect various types of measurements and send themeasurements to timeseries service 228. In some embodiments, eachmeasurement includes a measured value indicating a value of the measuredvariable and a timestamp indicating a time at which the variable wasmeasured. Feedback samples may also include monitored variables orstates of building equipment 1608, central plant equipment 1610, and/orIoT devices 203. For example, building equipment 1608, central plantequipment 1610, and/or IoT devices 203 may store internal variables thatrepresent equipment states (e.g., equipment on/off, door open/closed,equipment operating at 50% capacity, etc.). Each feedback sample mayinclude a value of a particular variable or state and a timestampindicating a time at which the variable or state was observed.

Timeseries service 228 is shown to include a timeseries identifier 1414,a DAG identifier 1416, execution engines 1330, and a timeseriesgenerator 1418. Timeseries identifier 1414 can receive the feedbacksamples from campus 1602 and can identify a timeseries associated witheach incoming data sample. The identified timeseries for a particularfeedback sample may be a feedback timeseries (i.e., a type of raw datatimeseries) stored in timeseries storage 214 that contains a series ofvalues for the same variable or data source. For example, feedbacktimeseries can be stored in the following format:

-   -   [<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key,        timestamp₃, value₃>]        where key is an identifier of the source of the feedback samples        (e.g., timeseries ID, sensor ID, etc.), timestamp identifies a        time associated with the ith sample, and value_(i) indicates the        value of the ith sample. Timeseries identifier 1414 can use        attributes of the incoming feedback samples (e.g., data source,        sensor ID, variable ID, etc.) to identify a particular feedback        timeseries associated with each sample and can provide the        identified timeseries ID to DAG identifier 1416.

DAG identifier 1416 can use the timeseries ID received from timeseriesidentifier 1414 to identify one or more feedback control DAGs that usethe identified feedback timeseries as an input. As described above, aDAG may be a predefined sequence of processing operations that transformone or more input timeseries into one or more output timeseries.Accordingly, each DAG may have one or more input timeseries associatedtherewith. A feedback control DAG is a type of DAG that defines afeedback control algorithm. For example, a feedback control DAG canaccept the feedback samples as an input and can define a sequence ofprocessing operations that transform the feedback samples into controlsignal samples using a feedback control technique. The processingoperations defined by a feedback control DAG can implement any of avariety of feedback control techniques including, for example,state-based control, extremum seeking control (ESC),proportional-integral (PI) control, proportional-integral-derivative(PID) control, model predictive control (MPC), or any other type offeedback control technique.

One example of a feedback control DAG is a PID control DAG. A PIDcontrol DAG may cause execution engines 1330 to perform a set ofprocessing operations typically performed by a PID controller. Forexample, the PID control DAG may cause execution engines 1330 tocalculate a difference between the feedback timeseries and a setpointtimeseries. The calculated difference can be saved as an errortimeseries. The PID control DAG may cause execution engines 1330 toapply a proportional gain to the error timeseries (e.g., multiplying theerror timeseries by a proportional gain parameter) to generate aproportional gain component of the control signal.

The PID control DAG may cause execution engines 1330 to integrate theerror timeseries over time to calculate an integrated error value foreach sample of the error timeseries. The integrated error values may besummations (i.e., numerical integrals) of the most recent error sampleand a predetermined number of previous error samples. In someembodiments, the integrated error values are saved as another derivedtimeseries (e.g., an integrated error timeseries). The PID control DAGmay cause execution engines 1330 to apply an integral gain to theintegrated error timeseries (e.g., multiplying the integrated errortimeseries by an integral gain parameter) to generate an integral gaincomponent of the control signal.

The PID control DAG may cause execution engines 1330 calculate aderivative error value for each sample of the error timeseries. Thederivative error values may be the slope or rate-of-change of the errortimeseries relative to the previous sample. In some embodiments, thederivative error values are saved as another derived timeseries (e.g., aderivative error timeseries). The PID control DAG may cause executionengines 1330 to apply a derivative gain to the derivative errortimeseries (e.g., multiplying the derivative error timeseries by aderivative gain parameter) to generate a derivative gain component ofthe control signal. The PID control DAG may cause execution engines 1330to combine (e.g., sum) the proportional gain component, the integralgain component, and the derivative gain component to generate a valuefor the next sample of the control signal timeseries.

In some embodiments, the feedback timeseries for each feedback controlDAG are stored as attributes of the feedback control DAG in DAG storage330. DAG identifier 1416 can read such information from DAG storage 330to determine which of the stored feedback control DAGs use theidentified feedback timeseries as an input. DAG identifier 1416 can thenprovide an indication of the identified feedback control DAGs toexecution engines 1330 in the form of one or more DAG IDs.

In some embodiments, DAG identifier 1416 also identifies any other inputtimeseries that are required as inputs to the identified feedbackcontrol DAGs. For example, a feedback control DAG for controlling thetemperature of a building space may have two inputs. The first input maybe a feedback timeseries that includes temperature measurementscollected from a temperature sensor within the building space. Thesecond input may be a setpoint timeseries that defines the temperaturesetpoint for the building space at each of a plurality of times. DAGidentifier 1416 can identify all of the timeseries that are required asinputs to the identified feedback control DAGs and can provide anindication of the identified timeseries to execution engines 1330 in theform of one or more timeseries IDs.

As described above, execution engines 1330 can include a C# engine 1332,a Python engine 1334, or any other engine configured to perform theoperations defined by a DAG. In some embodiments, execution engines 1330include timeseries operators 306. Execution engines 1330 can receive theincoming feedback samples from campus 1602, as well as the DAG IDs andtimeseries IDs from DAG identifier 1416. Execution engines 1330 canretrieve the identified feedback control DAGs from DAG storage 330 andcan retrieve the identified timeseries from timeseries storage 214 andexecute the feedback control DAGs, using the feedback samples as aninput, to generate control signal samples. Each control signal samplemay be the result of a timeseries processing operation that uses thefeedback samples (and possibly other timeseries samples) as an input. Insome embodiments, each control signal sample includes a key (e.g., atimeseries ID), a timestamp, and a value.

The timeseries retrieved from timeseries storage 214 may include samplesof any of the input timeseries required by one or more of the feedbackcontrol DAGs, including (in some instances) previous samples of thefeedback timeseries. For example, some types of feedback control such asPI control or PID control may require both the current value of afeedback signal (i.e., the most recent feedback sample) and one or morepast values of the feedback signal (i.e., one or more previous samplesof the same feedback timeseries) in order to generate a control signalsample (e.g., to evaluate an integrated error over time). Accordingly,execution engines 1330 may retrieve one or more past samples of thefeedback timeseries from timeseries storage 214. Execution engines 1330may use the past samples of the feedback timeseries in combination withthe current sample of the feedback timeseries and any other inputtimeseries to execute the feedback control DAG, thereby generatingcontrol signal samples.

Timeseries generator 1418 can use the control signal samples to generatea control signal timeseries. The control signal timeseries can be storedin timeseries storage 214 and/or provided as an output of web servicesplatform 102. Timeseries generator 1418 is shown providing a controlsignal to network 104 and campus 1602. The control signal can includethe value of the most recent control signal sample generated byexecuting the feedback control DAG and/or the control signal timeseriesgenerated by timeseries generator 1418. Campus 1602 can use the controlsignal to operate building equipment 1608, central plant equipment 1610,and/or IoT devices 203. For example, the control signal can be providedas an input to building equipment 1608 (e.g., via BMS 1604), centralplant equipment 1610 (e.g., via central plant 1606), and/or IoT devices203.

Advantageously, the cloud-based feedback control provided by webservices platform 102 can replace local (e.g., on-site or in-building)control loops typically used by conventional feedback control systems.Instead of requiring a local feedback controller to receive feedbackdata and generate control signals, the feedback data are provided as aninput to web services platform 102. Web services platform 102 identifiesand executes feedback control DAGs that provide the functionality of afeedback controller. The output of the feedback control DAGs are controlsignal samples that can be provided back to campus 102 and used tocontrol the equipment of campus 102.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

In various implementations, the steps and operations described hereinmay be performed on one processor or in a combination of two or moreprocessors. For example, in some implementations, the various operationscould be performed in a central server or set of central serversconfigured to receive data from one or more devices (e.g., edgecomputing devices/controllers) and perform the operations. In someimplementations, the operations may be performed by one or more localcontrollers or computing devices (e.g., edge devices), such ascontrollers dedicated to and/or located within a particular building orportion of a building. In some implementations, the operations may beperformed by a combination of one or more central or offsite computingdevices/servers and one or more local controllers/computing devices. Allsuch implementations are contemplated within the scope of the presentdisclosure. Further, unless otherwise indicated, when the presentdisclosure refers to one or more computer-readable storage media and/orone or more controllers, such computer-readable storage media and/or oneor more controllers may be implemented as one or more central servers,one or more local controllers or computing devices (e.g., edge devices),any combination thereof, or any other combination of storage mediaand/or controllers regardless of the location of such devices.

1-20. (canceled)
 21. A web platform comprising: one or more storagemedia storing instructions thereon that, when executed by one or moreprocessors, cause the one or more processors to: receive a timeseries ofdata values from a piece of equipment of an environment located remotefrom the web platform; select, from a plurality of control workflows, acontrol workflow that uses the timeseries of data values as an input anddefines one or more processing operations to be applied to thetimeseries of data values; execute the control workflow based on thetimeseries of data values to generate a control signal; and provide thecontrol signal to the piece of equipment of the environment, the pieceof equipment operating based on the control signal.
 22. The web platformof claim 21, wherein the timeseries of data values include a pluralityof feedback values measured by a sensor of the piece of equipment of theenvironment.
 23. The web platform of claim 21, wherein the controlsignal includes a timeseries comprising a set of control signal samples.24. The web platform of claim 23, wherein execution of the controlworkflow comprises: transforming one or more samples of the timeseriesof data values into one or more samples of the control signal byapplying the one or more samples of the timeseries of data values to thecontrol workflow; and assembling the one or more samples of the controlsignal to form a control signal timeseries.
 25. The web platform ofclaim 21, wherein the instructions cause the one or more processors to:identify one or more other timeseries required as inputs to the controlworkflow, wherein the one or more other timeseries comprise a setpointtimeseries comprising a plurality of setpoint samples, the plurality ofsetpoint samples defining setpoints corresponding to samples of thetimeseries of data values; and generate an enriched control workflowcomprising the control workflow, the timeseries of data values, and theone or more other timeseries.
 26. The web platform of claim 21, furthercomprising: a directed acyclic graph (DAG) database storing a pluralityof DAGs, each of the plurality of DAGs defining a particular controlworkflow; wherein the instructions cause the one or more processors to:determine whether any of the plurality of DAGs stored in the DAGdatabase are configured to use the timeseries of data values as a DAGinput.
 27. The web platform of claim 21, wherein the control workflowcomprises at least one of a state-based control workflow, an extremumseeking control (ESC) workflow, a proportional-integral (PI) controlworkflow, a proportional-integral-derivative (PID) control workflow, ora model predictive control (MPC) workflow to transform the timeseries ofdata values into the control signal using a control technique.
 28. Theweb platform of claim 21, wherein the instructions cause the one or moreprocessors to select the control workflow by: analyzing inputs of theplurality of control workflows including the control workflow; andselecting the control workflow from the plurality of control workflowsin response to a determination that the input of the control workflowutilizes the timeseries of data values.
 29. The web platform of claim21, wherein the instructions cause the one or more processors to executea proportional-integral-derivative (PID) control workflow to: generatean error timeseries comprising a plurality of error samples, theplurality of error samples indicating a difference between one sample ofthe timeseries of data values and a corresponding setpoint; and generatethe control signal by applying a set of PID control operations to theerror timeseries.
 30. The web platform of claim 27, wherein applying theset of PID control operations to the error timeseries comprises:generating an integrated error timeseries based on a plurality of errorsamples of the error timeseries; generating a derivative errortimeseries based on a change in value between consecutive samples of theerror timeseries; calculating a proportional gain component bymultiplying the error timeseries by a proportional gain parameter;calculating an integral gain component by multiplying the integratederror timeseries by an integral gain parameter; calculating a derivativegain component by multiplying the derivative error timeseries by aderivative gain parameter; and combining the proportional gaincomponent, the integral gain component, and the derivative gaincomponent to generate the control signal.
 31. A method comprising:receiving, by a cloud computing platform, a timeseries of data valuesfrom a piece of equipment of an environment located remote from thecloud computing system; selecting, by the cloud computing platform, froma plurality of control workflows, a control workflow that uses thetimeseries of data values as an input and defines one or more processingoperations to be applied to the timeseries of data values; executing, bythe cloud computing platform, the control workflow based on thetimeseries of data values to generate a control signal; and providing,by the cloud computing platform, the control signal to the piece ofequipment of the environment, the piece of equipment operating based onthe control signal.
 32. The method of claim 31, wherein the timeseriesof data values include a plurality of feedback values measured by asensor of the piece of equipment of the environment.
 33. The method ofclaim 31, wherein the control signal includes a timeseries comprising aset of control signal samples.
 34. The method of claim 33, whereinexecuting, by the cloud computing system, the control workflowcomprises: transforming one or more samples of the timeseries of datavalues into one or more samples of the control signal by applying theone or more samples of the timeseries of data values to the controlworkflow; and assembling the one or more samples of the control signalto form a control signal timeseries.
 35. The method of claim 31, furthercomprising: identifying, by the cloud computing system, one or moreother timeseries required as inputs to the control workflow, wherein theone or more other timeseries comprise a setpoint timeseries comprising aplurality of setpoint samples, the plurality of setpoint samplesdefining setpoints corresponding to samples of the timeseries of datavalues; and generating, by the cloud computing system, an enrichedcontrol workflow comprising the control workflow, the timeseries of datavalues, and the one or more other timeseries.
 36. The method of claim31, further comprising: determining, by the cloud computing platform,whether any of the plurality of DAGs stored in a DAG database storing aplurality of DAGs, each of the plurality of DAGs defining a particularcontrol workflow are configured to use the timeseries of data values asa DAG input.
 37. The method of claim 31, wherein the control workflowcomprises at least one of a state-based control workflow, an extremumseeking control (ESC) workflow, a proportional-integral (PI) controlworkflow, a proportional-integral-derivative (PID) control workflow, ora model predictive control (MPC) workflow to transform the timeseries ofdata values into the control signal using a control technique.
 38. Themethod of claim 31, wherein selecting, by the cloud computing system,the control workflow comprises: analyzing inputs of the plurality ofcontrol workflows including the control workflow; and selecting thecontrol workflow from the plurality of control workflows in response toa determination that the input of the control workflow utilizes thetimeseries of data values.
 39. The method of claim 31, furthercomprising executing, by the cloud computing platform, aproportional-integral-derivative (PID) control workflow to: generate anerror timeseries comprising a plurality of error samples, the pluralityof error samples indicating a difference between one sample of thetimeseries of data values and a corresponding setpoint; and generate thecontrol signal by applying a set of PID control operations to the errortimeseries.
 40. One or more computer readable media storing instructionsthereon that, when executed by one or more processors, cause the one ormore processors to: receive a timeseries of data values from a piece ofequipment of an environment; select, from a plurality of controlworkflows, a control workflow that uses the timeseries of data values asan input and defines one or more processing operations to be applied tothe timeseries of data values; execute the control workflow based on thetimeseries of data values to generate a control signal; and provide thecontrol signal to the piece of equipment of the environment, the pieceof equipment operating based on the control signal.